🛠️ MC-112253 is now fixed! (3377 days, 8 hours) 🛠️

Primitive NBT Lists that are not of type int need a type suffix

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🛠️ MC-176367 is now fixed! (2199 days, 18 hours, 44 minutes) 🛠️

Player inventory model spasms while trying to follow mouse

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🛠️ MC-208200 is now fixed! (1939 days, 15 hours, 58 minutes) 🛠️

/locatebiome in the same biome doesn't always return 0

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Snapshot backup warning is always shown if touchscreen mode is enabled

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🛠️ MC-169471 is now fixed! (2281 days, 40 minutes) 🛠️

Predicate errors show up as "loot table" in the log

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🛠️ MC-263865 is now fixed! (1012 days, 16 hours, 31 minutes) 🛠️

Fullscreen state isn't saved

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🛠️ MC-278853 is now fixed! (477 days, 22 hours, 13 minutes) 🛠️

Held items are invisible inside boats if "Improved Transparency" is enabled

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Player-controlled entity fails to teleport less than 10 blocks away

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The narrator prompts the user to use the Tab key three times in a row in scrollable menus

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🛠️ MC-263744 is now fixed! (1017 days, 14 hours, 29 minutes) 🛠️

/tp rotation not working for boat

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🛠️ MC-102608 is now fixed! (3599 days, 22 hours, 6 minutes) 🛠️

Player can't teleport its ridden entity sometimes

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Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis **DOI :****https://doi.org/10.5281/zenodo.19416373** Download Full-Text PDF Cite this Publication Milton Tomas Pedro Da Cruz, Prateek Kumar Srivastava, 2026, Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 2 * **Authors :** Milton Tomas Pedro Da Cruz, Prateek Kumar Srivastava * **Paper ID :** IJERTV15IS031515 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 04-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis Milton Tomas Pedro da Cruz, Prateek Kumar Srivastava School Of Computing Science And Engineering Galgotias University, Greater Noida, India Abstract – Our project addresses the increased complexities and occurrence of cyber threats through the creation of a real- time, web-based dashboard that would assist security personnel to identify, track, and assess attacks in real-time. On the front end, we inject React, Fast API on the back end, and MongoDB to give the threat intelligence a clean and user-friendly interface offering an interface to the analysts. All the alerts can be classified by severity, stream live events, interactive charts and alarms are all handled within the dashboard. We categorize the threats into four levels namely, critical, high, medium, and low in order to enable the teams to prioritize their responses. The dashboard proves to be responsive as our performance tests indicate that the dashboard is capable of managing high-frequency threat events. Its execution indicates how data-visualization libraries could be incorporated with real-time event processing algorithms to deliver actionable information to the SOC personnel. The work is an advancement of situational awareness instruments of cyber- security through integration of contemporary web development competencies with vital surveillance demands. Index Terms Cybersecurity, Threat Monitoring, Security Dash- board, Real- time Analytics, Web Application Security, Threat Intelligence, Security Operations Center, React, FastAPI. 1. INTRODUCTION 1. Background and Motivation We have been observing that the process of digitalizing businesses has contributed to the vastness of a target that cyber threats can strike. According to reports released in recent years, on average companies experience approximately 270 cyber attacks annually and the cost of cybercrime around the world is predicted to reach approximately 10.5 trillion dollars by 2025. [1] The traditional security monitoring systems are usually confusing in terms of interface, sluggishness in visualization of threat, as well as real-time response, and thus are difficult to manage the incidents in real-time. 2. Problem Statement The real truth is that I have been researching, how the existing cybersecurity monitoring solutions simply do not work in the real world. In the majority of works devoted to the subject, there is this detail that the increasing number of security alerts is in general overpowering analysts and hiding really essential threats, and it is really true. We are talking about some severe gaps. Information Overload Information: Security teams receive a torrent of alerts and it is almost impossible to actually determine which ones to bother with. Delayed Response The traditional systems do not have real time view and therefore the threats remain hidden unnoticed as long as possible and the reaction remains trapped in the past. complicated User Interfaces: The majority of enterprise tools are essentially a high Cost of Admission, which actually kills the productivity and makes the entire process stagnant. Lack of Contextual Information:Single standalone alert mes- sages lack any historic data on the alert (context) or trending, making it difficult to determine the real risk. Scalability Problems: The old back-end systems are unable to keep up with the massive speed and rate of attacks in the modern world, they are literally suffocating on the information. All this indicates that, we require monitoring tools that are not only capable of keeping pace in real-time and giving good context, but also ensuring that visuals are easily consumed such that a responder does not get lost in the maze 3. Objectives and Contributions This paper includes a web-based, modern-day cybersecurity dashboard, which addresses the challenges in the follow- ing ways. Real-Time Threat Monitoring: The adoption of a live feed of threats where they are updated within seconds of critical security events. Intelligent Severity Classification: There are four-tier severity model (critical, high, medium, low) on prioritized threat response. Interactive Visual Analytics: (D)ynamic charts and graphs which offer temporal threat anal- ysis and trends identification.Alert Management Their state: Alert tracking (status of active, under investigation, closed). Recent Technology Stack: Utilizing the current web-based technologies towards performance, scalability and maintain- ability. Interactive Design: Cross-ease of access that makes devices monitor. 4. Paper Organization The rest of this paper is structured as follows: Section II will perform a literature review on cybersecurity dashboard and threat monitoring systems. Section 3 illustrates system architecture and principles of the design. Section IV pro- vides an outline of the implementation plan and technological decisions. Section V talks about the system features and capabilities. The evaluation and analysis is in section VI. Section VII sums up the paper and provides research directions in the future. 2. RELATED WORK 1. Cybersecurity Dashboard Systems The field of cybersecurity visualization has been the topic of active research. Good all. essentially hit the ground by addressing the issue of displaying real-time data on matters concerning network security checks [2]. Their results indicate that an obvious sight can reduce the time that you require to identify danger. Commercial SIEM systems also abound, such as Splunk [3], IBM QRadar [4], AlienVault [5], to mention a few, and they provide good monitoring capabilities. However, they are also a burden to install, and tend to be very expensive, as well as the learning curve with smaller teams is normally a steep one. Nevertheless, have a look at Shiravi. They have demonstrated that lightweight, purpose-built dashboards can, in fact, be equally fast at certain kinds of jobs related to monitoring [6]. 2. Threat Intelligence Platforms. These threat intel were purely simple log aggregators, but this time they are very sophisticated analytical tools. The ME- TRE ATTACK is simply the ICT standard of choice anyway to categorize what is being done by the attackers [7]. Getting it in dashboards makes it less difficult to comprehend what happens to these threats, in fact. The crew and Wagner explored the combination of threat feeds and real-time monitoring setups, but they demonstrated that this combination does, in fact, increase the number of detections [8]. Threat intel is becoming more accessible with open-source tools, such as MISP [9] (the Malware Information Sharing Platform), OpenCTI [10], and so on. However, the thing is that actually, they are mostly connected with the informational sharing, but not with monitoring in the working process, thus there is this vacuum that can be filled by custom dashboards 3. Web Technologies in Security Applications An apparent trend in the construction of security surveil- lance systems is the use of current web technologies in cybersecurity tools. I have learned that studies of web-based security dashboards have found that reactive one-page web apps are significantly capable of enhancing user experience and efficiency of the analysts over traditional multipage sys- tems. This supports the demand. I found additional research on security data visualization, which show how hierarchical and time-based visualizations allow simplified understanding of complicated security data. Methods such as time-series, heat maps, and hierarchical displays can be used to improve situational awareness, which is why they are suitable for cy- bersecurity dashboards. Last but not least, recent publications on visual analytics as applied to Cybersecurity have acknowl- edged that interactive exploration is critical to facilitating the analytical finding of patterns, anomalies, and relation- ships within massive security databanks [11]. This supports the significance of the addition of interactive visualization to the contemporary security dashboard, which is what the proposed system is concentrating on regarding its design and adoption cite 14. In using a modern frontend framework to perform real-time security visualization.I found additional research on security data visualization, which shows how hierarchical and time-based visualizations allow simplified understanding of complicated security data. Methods such as time-series, heat maps, and hierarchical displays can be used to improve situational awareness, which is why they are suitable for cybersecurity dashboards. Last though not least, recent publications on visual analytics as applied to cybersecurity have acknowledged that interactive exploration is critical to facilitating the analytical finding of patterns, anomalies, and relationships within massive security databanks. This supports the significance of the addition of interactive visualization to the contemporary security dashboard, which is what the proposed system is concentrating on regarding its design and adoption. 4. Real-Time Data Processing The issue of responding to high-velocity streams of security events is rather wild, and during cybersecurity courses, instruc- tors discuss the various patterns of architectures. We have ob- served that event-driven architectures are, literally, super good when processing real-time flows of security data since they are scalable and responsive. This is the reason why stream- processing systems like Apache Kafka [12] and Apache Flink [13] are popular in security solutions in enterprises. Yet the study indicates that the practicality of distributed stream- processing systems can in fact be overruled by their extreme complexities of operation when it comes to implementing them in a mid-sized package. These structures tend to translate to a considerable amount of additional infrastructure, the bane of responsibility in configuration, and a lot of headaches in upkeep. Thus, this paper considers a middle way that delivers a contemporary web solution to achieve a close-to-real-time monitoring without the complexity of large-scale distributed stream-processing systems. 5. Research Gap Although we discover that the existing study and commer- cial applications provide robust security monitoring solutions, the number of available, lightweight, and up-to-date dashboard applications is evidently lacking. It happens that most security monitoring systems are more focused on the feature richness rather than the usability of their systems, which leads to difficult-to-use and maintain systems, which can be a concern for small organizations. This disconnect can be seen in the fact that scanty dashboards exist. Modernize web development and make the web work better. Have easy-to-use user interfaces with minimum training. Provide real-time monitoring without the need for complicated infrastructure. retain extensiveness and customization features. Support implementation at differ- ent levels of organizations. This gap is directly addressed by our work, where we introduce a cybersecurity dashboard that is a combination of modern web technologies and real security monitoring needs based on satisfying performance, usability, and scalability. 3. SYSTEM ARCHITECTURE 1. Architectural Overview. Consequently, the cybersecurity dashboard we will be dis- cussing is developed in a three-tier structure, which includes the presentation layer, the application layer, and the data layer. Separating things it makes it more modular, easier to scale, and simpler to maintain. In essence, all people are aware that layered architecture ensures that components are loosely coupled and the system progresses more easily since the responsibilities are separated. We have also adhered to principles of RESTful API and reactive programming, which enables data flow easily and also allow us to send real-time notifications. These sorts of design decisions are very effective with regard to security monitoring, where you have to remain responsive and scale up easily. 2. Technology Stack Selection The technology stack that I finally chose was partly in- formed by the following factors: performance, the developer ecosystem, security capabilities, scalability, and community support. Frontend Layer: React 18.3.1 In it is the primary UI framework. The magic of a virtual-DOM and the setup provided by components provides us with blistering per- formance on all those security dashboards that we require. TypeScript version 5.8.3: includes static typing to make the code dependable one and reduce the number of ugly bugs at a run time, which is essential to anything that is security intensive. Tailwind CSS 3.4.17: Utility-first, design that allows us to create the UI hyper-fast and at the same time be consistent across the board [15]. ShadCN UI Components: They are based on Radix UI primitives and are customizable, accessible, and adhere to the WAI-ARIA standard because we are not playing with compliance. Recharts 2.15.4: Provides the data-visualization layer, and, as such, provides interactive charts, which are helpful with security measurements. React Query (TanStack Query) 5.83.0: Does state management on the server, provides us with caching, background updates, and those optimistic UI effects that would make the application feel faster. 3. Component Architecture 1) Frontend Components: The front-end architecture is built on the principle of atomic design, which is designed with a hierarchy of components as follows: Pages: The feature-specific functionality is provided in containers (Route- level components, including, but not limited to: Dashboard, Threats, Alerts, Reports, and Settings). Composite Com- ponents: Special-purpose components such as ThreatChart, ThreatFeed, AlertsTable, and MetricCard are features of a more complex nature. UI Primitives: ShadCN UI provides reusable (Button, Card, Table, Badge) components, which are a consistent, styled, reusable experience. Hooks: Custom React hooks deal with cross-cutting points such as mobile responsiveness and toast notification. 2) Backend components, actually, a layered architecture is applied in the backend: API Layer: Endpoints that are prefixed with /api process whatever is received by and sent via HTTP. Service Layer Business logic will be placed here to modify information related to threat processing, tiering, and alerting. Data Access Layer: This layer is the operations of MongoDB abstracted with the interface of Motor. Models: Pydantic models rule out data schema definition of status checks, threats, and alerts 4. Security Considerations . The security measures that are part of the architecture are: CORS Setup: We have a controlled cross-origin resource sharing, and thus, there is nobody who can creep in. Input Validation: Pydantic models do not allow any type of data to be injected into them. Environment Variables: Environ- ment variables contain sensitive config, and it is not hard- coded. UUID Implementation: Objectids Prerequisites: This implementation list removes information leaks in sequential Objectids. Async Operations: Non-blocking I/O ensures that the system is immune to denial -of-service floods 5. Data Flow The system involves a one-directional flow of data: First of all, we feed security events using our endpoints to API backends. Then we authenticate such events and categorise them using severity. Events that are validated are stored in MongoDB, and each document has a timestamp and other metadata added. On the front, we can take updates by polling or subscribing to the endpoint points on the backend. React Query is used to manage the data caching and refresh the data. In case of changing the state, the components automatically re-render. And the last one, API calls are invoked with every user interaction, thereby repeating the same cycle 6. Scalability Design The architecture incorporates horizontal scale by Stateless Backend: FastAPI servers do not maintain any session state and therefore, we can freely have as many load-balanced instances without troubling. Indexing Data in Database Mon- goDB indexes on the fields of timestamp and the level of sever- ity blazingly appreciation the speed of queries, therefore our students are obliged to obtain data in MongoDB blazingly fast. Caching Strategy: React Query has intelligent caching, which reduces unnecessary API calls, thus saving bandwidth, and also, the UI is fast. Lazy Loading: Modular components and routes are loaded on-demand, the starting bundle is slick, and the application is light as a feather. 4. IMPLEMENTATION 1. Development Environment Setup The producing environment employs the contemporary tool- ing to. best incorporation developer experience: Vite 5.4.19: Are we using instant to build this tool? hot module replace- ment (HMR), production build optimization and server start. Uvicorn 0.25.0: executes the ASGI server of FastAPI, to support WebSocket connections in case of real-time. event streaming. Python 3.10 or later Python uses modern asyn- chronous functionality and type hints to have a good backend. 2. Core Features Implementation 1. Real-Time Dashboard: The primary dashboard (In- dex.tsx) equips all the key security indicators and presents them using the following components of MetricCards: Total Threats: this is the total number of threats that we have discovered including a percentage-change measure. Active Alerts: active alerts which must be addressed immediately providing the difference between the periods. Blocked Attacks: these are those attacks that we have been able to block and this is an indicator that the system is working. System Health: the general security posture score derived as a result of a number of measures. Each metric card shows: Value in an understandable format. A trend arrow (up or down) A context- fit icon ( checkmark, shield, activity, alert ). Color coded in terms of severity. 2. Classifying the Threat Severity: The system critically deems the severity under the four-tier severity model: Critical: urgent dangers which require quick action (do think live SQL injection or DDoS attacks). These we indicate by red (hsl(var(- critical)). High: severe threats, which must be analyzed as soon as possible (e.g., brute force attempts or suspicious logins). These go orange (hsl(var(-high))). Medium: possible security threats that should be monitored (e.g. port scanning, failed logins attempts). We make these yellow (hsl(var(-medium)). Low: minor anomalies only to be aware of them (e.g. small config adjustments). These are green (hsl(var(-low))). Such classification is in accord with industry norms and gives the workload of the analysts a priority, automatically. 3. Threat Activity Visualization: ThreatChart component provides a 7-day trends of threats in a stacked bar chart: Data Structure: This is the count of all four levels of severity per day, thus you can identify patterns. Interactive Elements: The Tooltip of Recharts provides information on hovering, and the CartesianGrid assists in reading the data. Responsive Design: ResponsiveContainer makes the chart fit inside the screen, and thus it can be used on any device. Color Consistency: the severity colors remain constant between components to ensure that the analysts do not get confused. This chart allows identifying such trends as the spikes of the attacks during weekends and weekdays, increases or decreases of the threat levels, the relations between the various severities, and the effectiveness of the countermeasures. 3. Backend API Implementation The FastAPI back-end provides us with a bunch of RESTful endpoints that we can easily make requests to on our end or the very front-end site. 1) Core Endpoints GET/api/- health check- a rapid examina- tion to determine whether the server is alive. POST/api/status- create a new status check money. GET /api/status – fetch down all of the entries of the status checks stored. 1. Database Integration We have Motor, which is the asynchronous MongoDB driver, thus the DB calls are not blocking and are fast. AsyncIOMotorClient on Motor does maintain a pool of persistent connections so that we are not required to pound on the database with each request. The IDs (as well) are all UUIDs, which makes the data JSON- friendly and they do not duplicate. The automatic validation and serialization provided by pydantic models is as follows:StatusCheckCreate System- The exchange schema against which incoming data shall be checked. StatusCheck- the response model that incorporates created fields such as the ID or date. 2. CORS and Security Implementation. The CORS middle- ware is configured such that allowed origins that are environ- mentally controlled can be added. Auth credentials We make it possible to send cookies or auth headers on authenticated requests. When we are developing we will be loose in allowing wildcard methods, wildcard header as long as we can, but when production comes around we tighten policies to adhere to the security best practice. 4. Routing and Navigation The application has a hybrid approach to state-management: State of server: React Query ensures that all data of the server is correct. It deal with automatic caching with a TTL that is customizable, background refetching so that things are always fresh, optimistic updating to make it seem fast, as well as, query invalidation to ensure all the data is consistent. UI State: The regular React state (useState, useReducer) is used for whatever stuff which is component local and relatively unimportant than, well, I dont know, you can use our next level of state instead. Global Context The react context API trades global issues like theme preferences and user settings. 5. Performance Optimization They make these optimization tricks make the app tight and fast to users! Code Splitting Asynchronous Code: We lazy load routes such that the first bundle will always be small and that pages only start popping up when you are actually going there. Memoization: Several of the example uses of React have useMemo and useCallback, which stops unnecessary recalcu- lations and re-rerendering, which is actually a way of making things fast. Debouncing: Search and filter inputs are debounced in which you will not end up spamming API calls when you hit a key. Virtual Scrolling: Big lists are made virtual to only display what you have to see and this is a massive performance improve on large data sets. Optimization of the Build: This is pulled together by the rollup setup of Vite: – Tree shaking in getting rid of unnec- essary code – Minification in order to reduce file sizes. Code splitting in order to cache better – Image optimization (images) Font optimization (fonts) 5. SYSTEM FEATURES AND CAPABILITIES 1. Dashboard Overview he primary dashboard provides me with a glance view of our security posture in the present time: KPIs: 4 major statistics. It gets 1,284 threats, 47 alerts and 3,892 blocked attacks and its Health is at 98.5 as of today with a trend arrows that allow you to easily discern that all things are changing. Visual Hierarchy: The information is arranged according to the level of importance, the most urgent things on the first page. Color Coding: The use of the same color palette on all components helps to strengthen the seriousness of things as well as types of statuses. Actionable Insights Every measure has a direct connection to a deeper dive panel to learn more 2. Threat Detection and Classification The system discovers and classifies different types of threats: Attack Types: – SQL Injection: Database exploiting. * DDoS Attacks: Distributed kill. – Brute Force: Cracking of pass- words. – Port Scanning: Network reconnaissance. – Mal- ware Detection: Virus detection. – Uncharacteristic login pat- terns: Abnormal activities in authentication. – Suspicious.exe Up- loads: This is potentially malicious con- tent Detection Methods: – Matching patterns with attack signa- ture patterns. * Abnormality detection of abnormal behavior. – Violations of rate limiting threshold. – Detection of anomalies using geolocation. – Comparison of file hash against threat databases. 3. Temporal Analysis Threat activity chart assists us to identify patterns: Trend Analysis: Alright, according to the 7-day moving window: – Threat volume fluctuations on a daily basis. – Highest attack times (i.e. Friday leads with 18 critical threats) – Weekend dips (Sat. 4 respectively Sunday 3) – The changes in severity on a weekly basis. Predictive Insights: On the basis of historical data we may predict: – Projected attack volume to capacity planning. – Repeating trends on how to be ahead. – Improved distribution of assets to prevent threats 4. Real-Time Monitoring The live threat feed is simply providing us with situational awareness on a continuous basis: Event Stream: chronolog- ically ranked threats with: – sub-minute update latency – temporal context (e.g. 2 min ago) in the form of relative timestamps. – automatically scroll to the including newest events – graphical symbols of new entries. Event Details: – source identification (Hostnames, IP addresses) – threat type classification – severity assessment – time of occurrence 6. EVALUATION AND RESULTS 1. Performance Metrics The assessment of the performance when the load varies con- conditions: Frontend Performance: 1.2s (3G network) – intial Page load – Time to Interactive – First Contentful Paint – Bundle size: 245KB (unused) – Lighthouse Performance Score points out to 94/100. Backend Performance – API Response Time averages: 45ms – 95th percentile: 120ms – Throughput Maximum: 1,000 requests/second – Database Query Response: Average 8ms Latency Timeliness: – Event Detection Records to Display: Less than 2 seconds- polling interval: 5 (config- urable) – Support percent: 100+ users at the same time. 2. Usability Assessment Unofficial testing with usability experts: Methodology: I have engaged 10 security analysts with varying levels of experience, and I have requested them to solve a few basic tasks: Find the most vital current threat Research a specific alert by ID Filter threats within the past 24 hours.investigate the weekly trends of the threats.update a specific alert to resolved. Findings: – Task completion rate: 98 percent -average time- to-task completion: 32 seconds -User satisfaction rating: 8.7/10 -learnability: It does not take much time to learn (less than 10 minutes) Qualitative Feedback: – Our current SIEM dashboard is much slower than this new one – The color coding enables priority evaluation immediately – Being able to access it through a mobile phone is a game-changer to response on- call – It should be more integrated with our ticketing system. 3. Scalability Analysis Results when loading the system: – 1,000 events/minute: There was no performance degradation, it completely re- mained smooth. – 5,000 events/minute: The 95 th percentile latency increased to 180ms; it is still controllable but not to overlook. – 10 000 events/ minute: Database indexing came into force and bottlenecks were avoided, enabling everything to run smoothly. Concurrent Users: – 50 simultaneous users: Stable perfor- mance – no hiccups. – 100 simultaneous users: Response time has slightly increased (mean time is 60ms), nevertheless, it is fine. – 200 active users: Horizontal scaling needed to ensure compliance. Data Volume: – 100 000 total events: The query performance remained good courtesy of indexing. – 1 million: There was an archival approach imple- mented on the older data. 10 million events – Data partitioning by time had been supported so that queries are efficient. 7. DISCUSSION 1. Key Contributions This study demonstrates that contemporary web technology can be utilized to do cybersecurity related work. The reactive style of React, with the scalable security dashboard offered by FastAPI and its aspyness and chop features, is a surprisingly super-responsive and would fit a wide variety of organiza- tional contexts. The four-layer severity framework, as well as various- dimensional pores, addresses the info overload break that is a nightmare in the old SIEM configurations. Through a sharp visual hierarchy and color indication of the mental load reduction, security analysts are able to prioritize things even when things get hot because of incident response. Division into architecture of presentation, application and data layers is what makes things maintainable and easily extending. Customizing the dashboard can also be accomplished by modifying single elements without disrupting the entire system and this is a major victory against the extended and cumber- some commercial solutions. 2. Practical Implications The reason why this dashboard is ideal in small to medium- sized organizations is that we have monitoring at an enterprise level and we have not incurred the huge licensing fees that come with commercial SIEMs; nor are we subjected to the complexity of the product. With the help of the modern tech stack – React and FastAPI – this thing remains up-to-date over the years and both frameworks have a healthy ecosystem and community. The responsive design allows SOC folks to have an eye on the threats in the case of any device, and thus react quicker even in off-hours. That is the mobile advantage that is highly beneficial in terms of distributed teams and remote workers. This can be used to teach at universities as a teaching re- source in classes of cybersec as students receive real hands-on experience with monitor interfaces and current dev practices. 3. Integration Opportunities In other words, the modular architecture allows connect- ing it to your existing security stack with minimal hard work:SIEM Integration: The backend supports API adapters to Splunk, QRadar, or an ELK stack. Threat Intelligence Feeds: It is possible to add MISP and GreenWood Threat intelligence feeds or commercial threat attack feeds freely to enhance the accuracy of the detection. Ticketing Systems: The connection with JIRA, Service Now, or any other ITSM allows one to smooth out incidents.Authentication: OAutp/ OIDC integration with corporate identity providers such as use of okta, azure advertisement and other keycloaks allows single- sign-on.Notification Systems: Webhooks and APIs may provide notifications either by email, SMS (Twilio), or by collaboration applications such as Slack or Microsoft Teams. 4. Technology Evolution The decision of the actively developed technologies of mod- ern type actually places this dashboard in the long-distance perspective: – React 19: The next React Server Components will enable us to perform hybrid rendering making the first page load time will go down. – FastAPI Evolution: Onwards improvement in performance and new features makes the back -end sound. – TypeScript Improvements: When the type inference and the editor work better, then we get to write code faster and can make lesser errors. – Web Platform APIs: Updated APIs such as Web- Transport have the potential to provide us even more low-latency real-time communication. 8. CONCLUSION AND FUTURE WORK 1. Summary This paper is my contribution to creating a full-scale cyber security dashboard that will be used to monitor and analyze threats on a real time basis. The use of modern web technologies, such as React, TypeScript, FastAPIs, and MongoDB, enables the system to provide a reactive and user-friendly system of security monitoring that would be appropriate in organizations of any size. The dashboard also effectively responds to the important issues of cybersecurity operations: information saturation via the smart use of filters, slow reaction due to the lack of timely information, and interface complexity through user-friendliness. The four-level severity classification with the support of visual analytics and the overall alert management, offers an appropriate control to threat response through an efficient actionable intelligence of security analysts. Performance analysis illustrates how the system can support high-frequency security events and yet be responsive to the interactions of the users. The usability testing shows that learning curves are low and user satisfaction is high, which proves the design that is user-friendly in nature. The well-architected, modular implementation makes sure that its improvements and additional integration with the existing security infrastructure can be extended in the future. Designing the dashboard with the current technologies puts the dashboard in a good place in the long-term maintenance and evolution 2. Future Research Directions The proposed cyber security dashboard can be enhanced in the future to include.More ML: ML is advanced analytics that would help you discover anomalies and predict atacks without being attacked, as well as understand how a user behaves like a data science project.Process Improvements: to get the updates more responding to a WebSocket, Server-Sent Events, and perform data process in the edges to make the updates appear more promptly than a chat group ping.Automated Response Automatic firewall rule adjustments and even system isolation whenever something is off.Threat Intelligence: ingest uniform threats feeds, and match indicators within seconds and add attribution to have a clue regarding who is orchestrating the bad guys.Sophisticated Visuals: Pile on network map, the geolocation overlay, even flow charts to be able to visualize attacks in a manner that will look good on a poster.Expansion of the platform: Develop mobile, desktop, and browser plat- forms such that the dashboard is wherever it is required by the squad.Team collaboration: team collaboration and event notes,as well as shift handover, such as a team notebook but security ops.Compliance and Reporting: Provide audit logs, regulatory reports and output customization tools to avoid headaches with compliance. 3. Closing Remarks Threats in the cyberspace are becoming much smarter and [14]thus, we require improved monitoring tools, but that does not imply that they must be extremely complicated. The article[15] demonstrates that, with the contemporary web technology, it is possible to create security isochronic with considerable power, but simple to utilize and maintain. Given that today cybersecurity has become a priority at any orga- nization, the availability of tools allowing making security data more understandable is increasingly important. At the interface of usefulness and robust technical characteristics, security teams are able to manage emerging threats with speed. It is a framework that has been developed based on a flexible design, and it provides a foundation on which further work and practical implementation can be developed, to create safer digital spaces. 9. ACKNOWLEDGMENT This is a great shout- out to the anonymous reviewers on their wise comments as this has enabled the improvement of the quality of this paper. This was aided by the Galgotias University Seed Grant Program. 10. REFERENCES 1. In 2024, a report was published by the Cybersecurity Ventures and it is titled 2024 Cybercrime Report: Global Cost of Cybercrime Predicted to Reach 10.5TrillionAnnually.It is an eye-opener article that makes one aware of the extent of the cost of cybercrime. 2. The title of this paper is Focusing on context in network traffic analysis. It was published in volume 25, issue 5, pages 72- 80, and published ‌back in 2005. 3. Their Splunk Enterprise Security Documentation is online. I snared it in 2023 and obtain it directly by accessing their site at the URL so https://www.splunk.com. 4. The IBM QRadar SIEM by IBM was also manufactured in 2023. The online documentation is available on the web at:https://www.ibm.com/qradar. 5. So all the details can be found at https://www.alienvault.com since the latest release of ATT Cybersecurity under the name AlienVault USM Platform 6. . In 2012, H. Shiravi, A. Shiravi, and A. A. Ghorbani published a survey, entitled A survey of visualization systems to network security. IEEE trans. Vis. Comput. Graphics, vol. 18, no. 8, p. 1313-1329. 7. In 2021, Mitchell Technologies issued their MITRE ATTCK Framework. The version available on the Internet is with the name attack.mitre.org. 8. C. Wagner, A. Dulaunoy, G. Wagener, and A. Iklody compiled an article named MISP: The design and implementation of collaborative threat intelligence sharing platform, 2016, 4956. 9. . In 2022, [9] CIRCL published MISP – Malware Information Sharing Platform, which is available online at https://www.misp-project.org. 10. The OpenCTI – Open Cyber Threat Intelligence Platform by Fil- igran is found on the Internet at the site of the portal, i.e. at https://www.openctI.io. 11. A survey. In 2020, it was published in machine learning with applica- tions, volume 6. 12. In 2007, A. DAmico, L. Buchanan, J. Goodall, and P. Walczak released an article titled as Mission impact of cyber events: Scenarios and ontology to bring out relationships between cyber assets, missions, and users and was in pages 388-397. 13. In 2018, M. Angelini, G. Santucci, H. Schumann, issue 5, volume 3 page 31. 14. In 2023, Apache Software .The documentation may be found on-line at https://kafka.apache.org. 15. Even bother Apache has published the document Apache Flink: Stateful Computations over Data Streams (2023, online at https://flink.apache.org). ______________

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Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis **DOI :****https://doi.org/10.5281/zenodo.19416373** Download Full-Text PDF Cite this Publication Milton Tomas Pedro Da Cruz, Prateek Kumar Srivastava, 2026, Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 2 * **Authors :** Milton Tomas Pedro Da Cruz, Prateek Kumar Srivastava * **Paper ID :** IJERTV15IS031515 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 04-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis Milton Tomas Pedro da Cruz, Prateek Kumar Srivastava School Of Computing Science And Engineering Galgotias University, Greater Noida, India Abstract – Our project addresses the increased complexities and occurrence of cyber threats through the creation of a real- time, web-based dashboard that would assist security personnel to identify, track, and assess attacks in real-time. On the front end, we inject React, Fast API on the back end, and MongoDB to give the threat intelligence a clean and user-friendly interface offering an interface to the analysts. All the alerts can be classified by severity, stream live events, interactive charts and alarms are all handled within the dashboard. We categorize the threats into four levels namely, critical, high, medium, and low in order to enable the teams to prioritize their responses. The dashboard proves to be responsive as our performance tests indicate that the dashboard is capable of managing high-frequency threat events. Its execution indicates how data-visualization libraries could be incorporated with real-time event processing algorithms to deliver actionable information to the SOC personnel. The work is an advancement of situational awareness instruments of cyber- security through integration of contemporary web development competencies with vital surveillance demands. Index Terms Cybersecurity, Threat Monitoring, Security Dash- board, Real- time Analytics, Web Application Security, Threat Intelligence, Security Operations Center, React, FastAPI. 1. INTRODUCTION 1. Background and Motivation We have been observing that the process of digitalizing businesses has contributed to the vastness of a target that cyber threats can strike. According to reports released in recent years, on average companies experience approximately 270 cyber attacks annually and the cost of cybercrime around the world is predicted to reach approximately 10.5 trillion dollars by 2025. [1] The traditional security monitoring systems are usually confusing in terms of interface, sluggishness in visualization of threat, as well as real-time response, and thus are difficult to manage the incidents in real-time. 2. Problem Statement The real truth is that I have been researching, how the existing cybersecurity monitoring solutions simply do not work in the real world. In the majority of works devoted to the subject, there is this detail that the increasing number of security alerts is in general overpowering analysts and hiding really essential threats, and it is really true. We are talking about some severe gaps. Information Overload Information: Security teams receive a torrent of alerts and it is almost impossible to actually determine which ones to bother with. Delayed Response The traditional systems do not have real time view and therefore the threats remain hidden unnoticed as long as possible and the reaction remains trapped in the past. complicated User Interfaces: The majority of enterprise tools are essentially a high Cost of Admission, which actually kills the productivity and makes the entire process stagnant. Lack of Contextual Information:Single standalone alert mes- sages lack any historic data on the alert (context) or trending, making it difficult to determine the real risk. Scalability Problems: The old back-end systems are unable to keep up with the massive speed and rate of attacks in the modern world, they are literally suffocating on the information. All this indicates that, we require monitoring tools that are not only capable of keeping pace in real-time and giving good context, but also ensuring that visuals are easily consumed such that a responder does not get lost in the maze 3. Objectives and Contributions This paper includes a web-based, modern-day cybersecurity dashboard, which addresses the challenges in the follow- ing ways. Real-Time Threat Monitoring: The adoption of a live feed of threats where they are updated within seconds of critical security events. Intelligent Severity Classification: There are four-tier severity model (critical, high, medium, low) on prioritized threat response. Interactive Visual Analytics: (D)ynamic charts and graphs which offer temporal threat anal- ysis and trends identification.Alert Management Their state: Alert tracking (status of active, under investigation, closed). Recent Technology Stack: Utilizing the current web-based technologies towards performance, scalability and maintain- ability. Interactive Design: Cross-ease of access that makes devices monitor. 4. Paper Organization The rest of this paper is structured as follows: Section II will perform a literature review on cybersecurity dashboard and threat monitoring systems. Section 3 illustrates system architecture and principles of the design. Section IV pro- vides an outline of the implementation plan and technological decisions. Section V talks about the system features and capabilities. The evaluation and analysis is in section VI. Section VII sums up the paper and provides research directions in the future. 2. RELATED WORK 1. Cybersecurity Dashboard Systems The field of cybersecurity visualization has been the topic of active research. Good all. essentially hit the ground by addressing the issue of displaying real-time data on matters concerning network security checks [2]. Their results indicate that an obvious sight can reduce the time that you require to identify danger. Commercial SIEM systems also abound, such as Splunk [3], IBM QRadar [4], AlienVault [5], to mention a few, and they provide good monitoring capabilities. However, they are also a burden to install, and tend to be very expensive, as well as the learning curve with smaller teams is normally a steep one. Nevertheless, have a look at Shiravi. They have demonstrated that lightweight, purpose-built dashboards can, in fact, be equally fast at certain kinds of jobs related to monitoring [6]. 2. Threat Intelligence Platforms. These threat intel were purely simple log aggregators, but this time they are very sophisticated analytical tools. The ME- TRE ATTACK is simply the ICT standard of choice anyway to categorize what is being done by the attackers [7]. Getting it in dashboards makes it less difficult to comprehend what happens to these threats, in fact. The crew and Wagner explored the combination of threat feeds and real-time monitoring setups, but they demonstrated that this combination does, in fact, increase the number of detections [8]. Threat intel is becoming more accessible with open-source tools, such as MISP [9] (the Malware Information Sharing Platform), OpenCTI [10], and so on. However, the thing is that actually, they are mostly connected with the informational sharing, but not with monitoring in the working process, thus there is this vacuum that can be filled by custom dashboards 3. Web Technologies in Security Applications An apparent trend in the construction of security surveil- lance systems is the use of current web technologies in cybersecurity tools. I have learned that studies of web-based security dashboards have found that reactive one-page web apps are significantly capable of enhancing user experience and efficiency of the analysts over traditional multipage sys- tems. This supports the demand. I found additional research on security data visualization, which show how hierarchical and time-based visualizations allow simplified understanding of complicated security data. Methods such as time-series, heat maps, and hierarchical displays can be used to improve situational awareness, which is why they are suitable for cy- bersecurity dashboards. Last but not least, recent publications on visual analytics as applied to Cybersecurity have acknowl- edged that interactive exploration is critical to facilitating the analytical finding of patterns, anomalies, and relation- ships within massive security databanks [11]. This supports the significance of the addition of interactive visualization to the contemporary security dashboard, which is what the proposed system is concentrating on regarding its design and adoption cite 14. In using a modern frontend framework to perform real-time security visualization.I found additional research on security data visualization, which shows how hierarchical and time-based visualizations allow simplified understanding of complicated security data. Methods such as time-series, heat maps, and hierarchical displays can be used to improve situational awareness, which is why they are suitable for cybersecurity dashboards. Last though not least, recent publications on visual analytics as applied to cybersecurity have acknowledged that interactive exploration is critical to facilitating the analytical finding of patterns, anomalies, and relationships within massive security databanks. This supports the significance of the addition of interactive visualization to the contemporary security dashboard, which is what the proposed system is concentrating on regarding its design and adoption. 4. Real-Time Data Processing The issue of responding to high-velocity streams of security events is rather wild, and during cybersecurity courses, instruc- tors discuss the various patterns of architectures. We have ob- served that event-driven architectures are, literally, super good when processing real-time flows of security data since they are scalable and responsive. This is the reason why stream- processing systems like Apache Kafka [12] and Apache Flink [13] are popular in security solutions in enterprises. Yet the study indicates that the practicality of distributed stream- processing systems can in fact be overruled by their extreme complexities of operation when it comes to implementing them in a mid-sized package. These structures tend to translate to a considerable amount of additional infrastructure, the bane of responsibility in configuration, and a lot of headaches in upkeep. Thus, this paper considers a middle way that delivers a contemporary web solution to achieve a close-to-real-time monitoring without the complexity of large-scale distributed stream-processing systems. 5. Research Gap Although we discover that the existing study and commer- cial applications provide robust security monitoring solutions, the number of available, lightweight, and up-to-date dashboard applications is evidently lacking. It happens that most security monitoring systems are more focused on the feature richness rather than the usability of their systems, which leads to difficult-to-use and maintain systems, which can be a concern for small organizations. This disconnect can be seen in the fact that scanty dashboards exist. Modernize web development and make the web work better. Have easy-to-use user interfaces with minimum training. Provide real-time monitoring without the need for complicated infrastructure. retain extensiveness and customization features. Support implementation at differ- ent levels of organizations. This gap is directly addressed by our work, where we introduce a cybersecurity dashboard that is a combination of modern web technologies and real security monitoring needs based on satisfying performance, usability, and scalability. 3. SYSTEM ARCHITECTURE 1. Architectural Overview. Consequently, the cybersecurity dashboard we will be dis- cussing is developed in a three-tier structure, which includes the presentation layer, the application layer, and the data layer. Separating things it makes it more modular, easier to scale, and simpler to maintain. In essence, all people are aware that layered architecture ensures that components are loosely coupled and the system progresses more easily since the responsibilities are separated. We have also adhered to principles of RESTful API and reactive programming, which enables data flow easily and also allow us to send real-time notifications. These sorts of design decisions are very effective with regard to security monitoring, where you have to remain responsive and scale up easily. 2. Technology Stack Selection The technology stack that I finally chose was partly in- formed by the following factors: performance, the developer ecosystem, security capabilities, scalability, and community support. Frontend Layer: React 18.3.1 In it is the primary UI framework. The magic of a virtual-DOM and the setup provided by components provides us with blistering per- formance on all those security dashboards that we require. TypeScript version 5.8.3: includes static typing to make the code dependable one and reduce the number of ugly bugs at a run time, which is essential to anything that is security intensive. Tailwind CSS 3.4.17: Utility-first, design that allows us to create the UI hyper-fast and at the same time be consistent across the board [15]. ShadCN UI Components: They are based on Radix UI primitives and are customizable, accessible, and adhere to the WAI-ARIA standard because we are not playing with compliance. Recharts 2.15.4: Provides the data-visualization layer, and, as such, provides interactive charts, which are helpful with security measurements. React Query (TanStack Query) 5.83.0: Does state management on the server, provides us with caching, background updates, and those optimistic UI effects that would make the application feel faster. 3. Component Architecture 1) Frontend Components: The front-end architecture is built on the principle of atomic design, which is designed with a hierarchy of components as follows: Pages: The feature-specific functionality is provided in containers (Route- level components, including, but not limited to: Dashboard, Threats, Alerts, Reports, and Settings). Composite Com- ponents: Special-purpose components such as ThreatChart, ThreatFeed, AlertsTable, and MetricCard are features of a more complex nature. UI Primitives: ShadCN UI provides reusable (Button, Card, Table, Badge) components, which are a consistent, styled, reusable experience. Hooks: Custom React hooks deal with cross-cutting points such as mobile responsiveness and toast notification. 2) Backend components, actually, a layered architecture is applied in the backend: API Layer: Endpoints that are prefixed with /api process whatever is received by and sent via HTTP. Service Layer Business logic will be placed here to modify information related to threat processing, tiering, and alerting. Data Access Layer: This layer is the operations of MongoDB abstracted with the interface of Motor. Models: Pydantic models rule out data schema definition of status checks, threats, and alerts 4. Security Considerations . The security measures that are part of the architecture are: CORS Setup: We have a controlled cross-origin resource sharing, and thus, there is nobody who can creep in. Input Validation: Pydantic models do not allow any type of data to be injected into them. Environment Variables: Environ- ment variables contain sensitive config, and it is not hard- coded. UUID Implementation: Objectids Prerequisites: This implementation list removes information leaks in sequential Objectids. Async Operations: Non-blocking I/O ensures that the system is immune to denial -of-service floods 5. Data Flow The system involves a one-directional flow of data: First of all, we feed security events using our endpoints to API backends. Then we authenticate such events and categorise them using severity. Events that are validated are stored in MongoDB, and each document has a timestamp and other metadata added. On the front, we can take updates by polling or subscribing to the endpoint points on the backend. React Query is used to manage the data caching and refresh the data. In case of changing the state, the components automatically re-render. And the last one, API calls are invoked with every user interaction, thereby repeating the same cycle 6. Scalability Design The architecture incorporates horizontal scale by Stateless Backend: FastAPI servers do not maintain any session state and therefore, we can freely have as many load-balanced instances without troubling. Indexing Data in Database Mon- goDB indexes on the fields of timestamp and the level of sever- ity blazingly appreciation the speed of queries, therefore our students are obliged to obtain data in MongoDB blazingly fast. Caching Strategy: React Query has intelligent caching, which reduces unnecessary API calls, thus saving bandwidth, and also, the UI is fast. Lazy Loading: Modular components and routes are loaded on-demand, the starting bundle is slick, and the application is light as a feather. 4. IMPLEMENTATION 1. Development Environment Setup The producing environment employs the contemporary tool- ing to. best incorporation developer experience: Vite 5.4.19: Are we using instant to build this tool? hot module replace- ment (HMR), production build optimization and server start. Uvicorn 0.25.0: executes the ASGI server of FastAPI, to support WebSocket connections in case of real-time. event streaming. Python 3.10 or later Python uses modern asyn- chronous functionality and type hints to have a good backend. 2. Core Features Implementation 1. Real-Time Dashboard: The primary dashboard (In- dex.tsx) equips all the key security indicators and presents them using the following components of MetricCards: Total Threats: this is the total number of threats that we have discovered including a percentage-change measure. Active Alerts: active alerts which must be addressed immediately providing the difference between the periods. Blocked Attacks: these are those attacks that we have been able to block and this is an indicator that the system is working. System Health: the general security posture score derived as a result of a number of measures. Each metric card shows: Value in an understandable format. A trend arrow (up or down) A context- fit icon ( checkmark, shield, activity, alert ). Color coded in terms of severity. 2. Classifying the Threat Severity: The system critically deems the severity under the four-tier severity model: Critical: urgent dangers which require quick action (do think live SQL injection or DDoS attacks). These we indicate by red (hsl(var(- critical)). High: severe threats, which must be analyzed as soon as possible (e.g., brute force attempts or suspicious logins). These go orange (hsl(var(-high))). Medium: possible security threats that should be monitored (e.g. port scanning, failed logins attempts). We make these yellow (hsl(var(-medium)). Low: minor anomalies only to be aware of them (e.g. small config adjustments). These are green (hsl(var(-low))). Such classification is in accord with industry norms and gives the workload of the analysts a priority, automatically. 3. Threat Activity Visualization: ThreatChart component provides a 7-day trends of threats in a stacked bar chart: Data Structure: This is the count of all four levels of severity per day, thus you can identify patterns. Interactive Elements: The Tooltip of Recharts provides information on hovering, and the CartesianGrid assists in reading the data. Responsive Design: ResponsiveContainer makes the chart fit inside the screen, and thus it can be used on any device. Color Consistency: the severity colors remain constant between components to ensure that the analysts do not get confused. This chart allows identifying such trends as the spikes of the attacks during weekends and weekdays, increases or decreases of the threat levels, the relations between the various severities, and the effectiveness of the countermeasures. 3. Backend API Implementation The FastAPI back-end provides us with a bunch of RESTful endpoints that we can easily make requests to on our end or the very front-end site. 1) Core Endpoints GET/api/- health check- a rapid examina- tion to determine whether the server is alive. POST/api/status- create a new status check money. GET /api/status – fetch down all of the entries of the status checks stored. 1. Database Integration We have Motor, which is the asynchronous MongoDB driver, thus the DB calls are not blocking and are fast. AsyncIOMotorClient on Motor does maintain a pool of persistent connections so that we are not required to pound on the database with each request. The IDs (as well) are all UUIDs, which makes the data JSON- friendly and they do not duplicate. The automatic validation and serialization provided by pydantic models is as follows:StatusCheckCreate System- The exchange schema against which incoming data shall be checked. StatusCheck- the response model that incorporates created fields such as the ID or date. 2. CORS and Security Implementation. The CORS middle- ware is configured such that allowed origins that are environ- mentally controlled can be added. Auth credentials We make it possible to send cookies or auth headers on authenticated requests. When we are developing we will be loose in allowing wildcard methods, wildcard header as long as we can, but when production comes around we tighten policies to adhere to the security best practice. 4. Routing and Navigation The application has a hybrid approach to state-management: State of server: React Query ensures that all data of the server is correct. It deal with automatic caching with a TTL that is customizable, background refetching so that things are always fresh, optimistic updating to make it seem fast, as well as, query invalidation to ensure all the data is consistent. UI State: The regular React state (useState, useReducer) is used for whatever stuff which is component local and relatively unimportant than, well, I dont know, you can use our next level of state instead. Global Context The react context API trades global issues like theme preferences and user settings. 5. Performance Optimization They make these optimization tricks make the app tight and fast to users! Code Splitting Asynchronous Code: We lazy load routes such that the first bundle will always be small and that pages only start popping up when you are actually going there. Memoization: Several of the example uses of React have useMemo and useCallback, which stops unnecessary recalcu- lations and re-rerendering, which is actually a way of making things fast. Debouncing: Search and filter inputs are debounced in which you will not end up spamming API calls when you hit a key. Virtual Scrolling: Big lists are made virtual to only display what you have to see and this is a massive performance improve on large data sets. Optimization of the Build: This is pulled together by the rollup setup of Vite: – Tree shaking in getting rid of unnec- essary code – Minification in order to reduce file sizes. Code splitting in order to cache better – Image optimization (images) Font optimization (fonts) 5. SYSTEM FEATURES AND CAPABILITIES 1. Dashboard Overview he primary dashboard provides me with a glance view of our security posture in the present time: KPIs: 4 major statistics. It gets 1,284 threats, 47 alerts and 3,892 blocked attacks and its Health is at 98.5 as of today with a trend arrows that allow you to easily discern that all things are changing. Visual Hierarchy: The information is arranged according to the level of importance, the most urgent things on the first page. Color Coding: The use of the same color palette on all components helps to strengthen the seriousness of things as well as types of statuses. Actionable Insights Every measure has a direct connection to a deeper dive panel to learn more 2. Threat Detection and Classification The system discovers and classifies different types of threats: Attack Types: – SQL Injection: Database exploiting. * DDoS Attacks: Distributed kill. – Brute Force: Cracking of pass- words. – Port Scanning: Network reconnaissance. – Mal- ware Detection: Virus detection. – Uncharacteristic login pat- terns: Abnormal activities in authentication. – Suspicious.exe Up- loads: This is potentially malicious con- tent Detection Methods: – Matching patterns with attack signa- ture patterns. * Abnormality detection of abnormal behavior. – Violations of rate limiting threshold. – Detection of anomalies using geolocation. – Comparison of file hash against threat databases. 3. Temporal Analysis Threat activity chart assists us to identify patterns: Trend Analysis: Alright, according to the 7-day moving window: – Threat volume fluctuations on a daily basis. – Highest attack times (i.e. Friday leads with 18 critical threats) – Weekend dips (Sat. 4 respectively Sunday 3) – The changes in severity on a weekly basis. Predictive Insights: On the basis of historical data we may predict: – Projected attack volume to capacity planning. – Repeating trends on how to be ahead. – Improved distribution of assets to prevent threats 4. Real-Time Monitoring The live threat feed is simply providing us with situational awareness on a continuous basis: Event Stream: chronolog- ically ranked threats with: – sub-minute update latency – temporal context (e.g. 2 min ago) in the form of relative timestamps. – automatically scroll to the including newest events – graphical symbols of new entries. Event Details: – source identification (Hostnames, IP addresses) – threat type classification – severity assessment – time of occurrence 6. EVALUATION AND RESULTS 1. Performance Metrics The assessment of the performance when the load varies con- conditions: Frontend Performance: 1.2s (3G network) – intial Page load – Time to Interactive – First Contentful Paint – Bundle size: 245KB (unused) – Lighthouse Performance Score points out to 94/100. Backend Performance – API Response Time averages: 45ms – 95th percentile: 120ms – Throughput Maximum: 1,000 requests/second – Database Query Response: Average 8ms Latency Timeliness: – Event Detection Records to Display: Less than 2 seconds- polling interval: 5 (config- urable) – Support percent: 100+ users at the same time. 2. Usability Assessment Unofficial testing with usability experts: Methodology: I have engaged 10 security analysts with varying levels of experience, and I have requested them to solve a few basic tasks: Find the most vital current threat Research a specific alert by ID Filter threats within the past 24 hours.investigate the weekly trends of the threats.update a specific alert to resolved. Findings: – Task completion rate: 98 percent -average time- to-task completion: 32 seconds -User satisfaction rating: 8.7/10 -learnability: It does not take much time to learn (less than 10 minutes) Qualitative Feedback: – Our current SIEM dashboard is much slower than this new one – The color coding enables priority evaluation immediately – Being able to access it through a mobile phone is a game-changer to response on- call – It should be more integrated with our ticketing system. 3. Scalability Analysis Results when loading the system: – 1,000 events/minute: There was no performance degradation, it completely re- mained smooth. – 5,000 events/minute: The 95 th percentile latency increased to 180ms; it is still controllable but not to overlook. – 10 000 events/ minute: Database indexing came into force and bottlenecks were avoided, enabling everything to run smoothly. Concurrent Users: – 50 simultaneous users: Stable perfor- mance – no hiccups. – 100 simultaneous users: Response time has slightly increased (mean time is 60ms), nevertheless, it is fine. – 200 active users: Horizontal scaling needed to ensure compliance. Data Volume: – 100 000 total events: The query performance remained good courtesy of indexing. – 1 million: There was an archival approach imple- mented on the older data. 10 million events – Data partitioning by time had been supported so that queries are efficient. 7. DISCUSSION 1. Key Contributions This study demonstrates that contemporary web technology can be utilized to do cybersecurity related work. The reactive style of React, with the scalable security dashboard offered by FastAPI and its aspyness and chop features, is a surprisingly super-responsive and would fit a wide variety of organiza- tional contexts. The four-layer severity framework, as well as various- dimensional pores, addresses the info overload break that is a nightmare in the old SIEM configurations. Through a sharp visual hierarchy and color indication of the mental load reduction, security analysts are able to prioritize things even when things get hot because of incident response. Division into architecture of presentation, application and data layers is what makes things maintainable and easily extending. Customizing the dashboard can also be accomplished by modifying single elements without disrupting the entire system and this is a major victory against the extended and cumber- some commercial solutions. 2. Practical Implications The reason why this dashboard is ideal in small to medium- sized organizations is that we have monitoring at an enterprise level and we have not incurred the huge licensing fees that come with commercial SIEMs; nor are we subjected to the complexity of the product. With the help of the modern tech stack – React and FastAPI – this thing remains up-to-date over the years and both frameworks have a healthy ecosystem and community. The responsive design allows SOC folks to have an eye on the threats in the case of any device, and thus react quicker even in off-hours. That is the mobile advantage that is highly beneficial in terms of distributed teams and remote workers. This can be used to teach at universities as a teaching re- source in classes of cybersec as students receive real hands-on experience with monitor interfaces and current dev practices. 3. Integration Opportunities In other words, the modular architecture allows connect- ing it to your existing security stack with minimal hard work:SIEM Integration: The backend supports API adapters to Splunk, QRadar, or an ELK stack. Threat Intelligence Feeds: It is possible to add MISP and GreenWood Threat intelligence feeds or commercial threat attack feeds freely to enhance the accuracy of the detection. Ticketing Systems: The connection with JIRA, Service Now, or any other ITSM allows one to smooth out incidents.Authentication: OAutp/ OIDC integration with corporate identity providers such as use of okta, azure advertisement and other keycloaks allows single- sign-on.Notification Systems: Webhooks and APIs may provide notifications either by email, SMS (Twilio), or by collaboration applications such as Slack or Microsoft Teams. 4. Technology Evolution The decision of the actively developed technologies of mod- ern type actually places this dashboard in the long-distance perspective: – React 19: The next React Server Components will enable us to perform hybrid rendering making the first page load time will go down. – FastAPI Evolution: Onwards improvement in performance and new features makes the back -end sound. – TypeScript Improvements: When the type inference and the editor work better, then we get to write code faster and can make lesser errors. – Web Platform APIs: Updated APIs such as Web- Transport have the potential to provide us even more low-latency real-time communication. 8. CONCLUSION AND FUTURE WORK 1. Summary This paper is my contribution to creating a full-scale cyber security dashboard that will be used to monitor and analyze threats on a real time basis. The use of modern web technologies, such as React, TypeScript, FastAPIs, and MongoDB, enables the system to provide a reactive and user-friendly system of security monitoring that would be appropriate in organizations of any size. The dashboard also effectively responds to the important issues of cybersecurity operations: information saturation via the smart use of filters, slow reaction due to the lack of timely information, and interface complexity through user-friendliness. The four-level severity classification with the support of visual analytics and the overall alert management, offers an appropriate control to threat response through an efficient actionable intelligence of security analysts. Performance analysis illustrates how the system can support high-frequency security events and yet be responsive to the interactions of the users. The usability testing shows that learning curves are low and user satisfaction is high, which proves the design that is user-friendly in nature. The well-architected, modular implementation makes sure that its improvements and additional integration with the existing security infrastructure can be extended in the future. Designing the dashboard with the current technologies puts the dashboard in a good place in the long-term maintenance and evolution 2. Future Research Directions The proposed cyber security dashboard can be enhanced in the future to include.More ML: ML is advanced analytics that would help you discover anomalies and predict atacks without being attacked, as well as understand how a user behaves like a data science project.Process Improvements: to get the updates more responding to a WebSocket, Server-Sent Events, and perform data process in the edges to make the updates appear more promptly than a chat group ping.Automated Response Automatic firewall rule adjustments and even system isolation whenever something is off.Threat Intelligence: ingest uniform threats feeds, and match indicators within seconds and add attribution to have a clue regarding who is orchestrating the bad guys.Sophisticated Visuals: Pile on network map, the geolocation overlay, even flow charts to be able to visualize attacks in a manner that will look good on a poster.Expansion of the platform: Develop mobile, desktop, and browser plat- forms such that the dashboard is wherever it is required by the squad.Team collaboration: team collaboration and event notes,as well as shift handover, such as a team notebook but security ops.Compliance and Reporting: Provide audit logs, regulatory reports and output customization tools to avoid headaches with compliance. 3. Closing Remarks Threats in the cyberspace are becoming much smarter and [14]thus, we require improved monitoring tools, but that does not imply that they must be extremely complicated. The article[15] demonstrates that, with the contemporary web technology, it is possible to create security isochronic with considerable power, but simple to utilize and maintain. Given that today cybersecurity has become a priority at any orga- nization, the availability of tools allowing making security data more understandable is increasingly important. At the interface of usefulness and robust technical characteristics, security teams are able to manage emerging threats with speed. It is a framework that has been developed based on a flexible design, and it provides a foundation on which further work and practical implementation can be developed, to create safer digital spaces. 9. ACKNOWLEDGMENT This is a great shout- out to the anonymous reviewers on their wise comments as this has enabled the improvement of the quality of this paper. This was aided by the Galgotias University Seed Grant Program. 10. REFERENCES 1. In 2024, a report was published by the Cybersecurity Ventures and it is titled 2024 Cybercrime Report: Global Cost of Cybercrime Predicted to Reach 10.5TrillionAnnually.It is an eye-opener article that makes one aware of the extent of the cost of cybercrime. 2. The title of this paper is Focusing on context in network traffic analysis. It was published in volume 25, issue 5, pages 72- 80, and published ‌back in 2005. 3. Their Splunk Enterprise Security Documentation is online. I snared it in 2023 and obtain it directly by accessing their site at the URL so https://www.splunk.com. 4. The IBM QRadar SIEM by IBM was also manufactured in 2023. The online documentation is available on the web at:https://www.ibm.com/qradar. 5. So all the details can be found at https://www.alienvault.com since the latest release of ATT Cybersecurity under the name AlienVault USM Platform 6. . In 2012, H. Shiravi, A. Shiravi, and A. A. Ghorbani published a survey, entitled A survey of visualization systems to network security. IEEE trans. Vis. Comput. Graphics, vol. 18, no. 8, p. 1313-1329. 7. In 2021, Mitchell Technologies issued their MITRE ATTCK Framework. The version available on the Internet is with the name attack.mitre.org. 8. C. Wagner, A. Dulaunoy, G. Wagener, and A. Iklody compiled an article named MISP: The design and implementation of collaborative threat intelligence sharing platform, 2016, 4956. 9. . In 2022, [9] CIRCL published MISP – Malware Information Sharing Platform, which is available online at https://www.misp-project.org. 10. The OpenCTI – Open Cyber Threat Intelligence Platform by Fil- igran is found on the Internet at the site of the portal, i.e. at https://www.openctI.io. 11. A survey. In 2020, it was published in machine learning with applica- tions, volume 6. 12. In 2007, A. DAmico, L. Buchanan, J. Goodall, and P. Walczak released an article titled as Mission impact of cyber events: Scenarios and ontology to bring out relationships between cyber assets, missions, and users and was in pages 388-397. 13. In 2018, M. Angelini, G. Santucci, H. Schumann, issue 5, volume 3 page 31. 14. In 2023, Apache Software .The documentation may be found on-line at https://kafka.apache.org. 15. Even bother Apache has published the document Apache Flink: Stateful Computations over Data Streams (2023, online at https://flink.apache.org). ______________

Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analysis View Abstract & download full text of Real-Time Cyber Security Dashboard for Advanced Threat Monitoring and Analys...

#Volume #15, #Issue #03 #(March #2026)

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🛠️ MC-123605 is now fixed! (3019 days, 11 hours, 41 minutes) 🛠️

Debug world still sets clear weather time instead of deactivating gamerule doWeatherCycle

➡️ https://bugs.mojang.com/browse/MC-123605

Original post on kittsteiner.blog

TIL: Energy Efficient Ethernet (EEE) in the newest firmware (8.2x) of my FritzBox is probably garbage. When set to “automatic” (which is the only setting aside of “off”), the network is super unstable between devices on the LAN/WAN and any other device. In both the LAN and the internet.

Clear […]

Gamified Cybersecurity Awareness System: An Interactive Approach to Digital Safety **DOI :****https://doi.org/10.5281/zenodo.19402013** Download Full-Text PDF Cite this Publication Mitali Chopade, Samiksha Patil, Dr. (Mrs) Jaya Terdale, Sakshi More, Mayuri Gote, 2026, Gamified Cybersecurity Awareness System: An Interactive Approach to Digital Safety, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** Mitali Chopade, Samiksha Patil, Dr. (Mrs) Jaya Terdale, Sakshi More, Mayuri Gote * **Paper ID :** IJERTV15IS031386 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 03-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Gamified Cybersecurity Awareness System: An Interactive Approach to Digital Safety Mitali Chopade Department of Artificial Intelligence and Data Science, A. C. Patil College of Engineering, University of Mumbai Samiksha Patil Department of Artificial Intelligence and Data Science, A. C. Patil College of Engineering, University of Mumbai Dr. (Mrs) Jaya Terdale Department of Artificial Intelligence and Data Science, A. C. Patil College of Engineering, University of Mumbai Sakshi More Department of Artificial Intelligence and Data Science, A. C. Patil College of Engineering, University of Mumbai Mayuri Gote Department of Artificial Intelligence and Data Science, A. C. Patil College of Engineering, University of Mumbai Abstract – In the present digital era, cybercrimes such as phishing, ransomware, and social engineering are rapidly increasing, yet user unawareness remains a leading cause of data breaches. Traditional cybersecurity awareness programs often fail to hold learners attention, resulting in poor knowledge retention and minimal behavioural change. To overcome this limitation, this paper proposes a Gamified Cybersecurity Awareness System, a dynamic platform designed to educate users through engaging gamification techniques. The system transforms theoretical concepts into practical challenges, utilizing a chatbot-based tutor to guide users in identifying threats. By integrating game mechanics like points, badges, and leader boards, the system fosters motivation and healthy competition. Designed to be inclusive and scalable, it aims to cultivate a culture of cybersecurity awareness, empowering individuals to navigate the online world safely. Keywords Gamification, Cybersecurity, Awareness, Interactive Learning, Chatbot, Phishing, Social Engineering. 1. INTRODUCTION Cybersecurity has emerged as a critical concern globally. According to CERT-In, incidents of phishing and digital fraud have risen exponentially. While security software exists, the “human element” remains the weakest link, with over 90% of breaches attributed to human error. In digital ecosystem, the integration of internet-based services into financial, professional, and personal domains has expanded the attack surface for malicious actors Empirical studies indicate that a significant majority of cybersecurity breaches often exceeding 90% are precipitated by human error rather than technical failures, with users frequently falling victim to social engineering, phishing, and weak credential management. Consequently, cybersecurity awareness has transitioned from a specialized requirement to a fundamental digital literacy skill. Despite the urgency of this requirement, conventional approaches to cybersecurity training are characterized by passive lectures, static documentation, and compliance-based workshops that often fail to foster active engagement or long-term knowledge retention. These traditional models prioritize theoretical memorization over practical application, leaving users ill-equipped to identify and neutralize sophisticated real- world threats. To address these limitations, this research presents a Gamified Cybersecurity Awareness System, a web-based platform that synthesizes Security Education, Training, and Awareness (SETA) principles with interactive game mechanics. Moreover, this research addresses the critical need for an inclusive and actionable safety tool. Recognizing that digital threats target individuals across all age groups and technical backgrounds, the system is designed to be accessible to a diverse demographic, including students and senior citizens who are often disproportionately vulnerable. Uniquely, the platform extends beyond education by integrating direct pathways to national incident reporting mechanisms, thereby empowering users to translate their awareness into real-world remedial action. 1. Motivation The primary motivation for this research stems from the persistent disconnect between theoretical cybersecurity knowledge and its practical application. A notable real-world incident involving employees at a pharmaceutical company, who were deceived by fraudulent HR emails during salary increments, illustrates that even educated professionals remain vulnerable to social engineering. This example underscores that traditional, passive awareness methods are insufficient against sophisticated psychological manipulation, highlighting a critical need for training that focuses on behavioural change rather than simple memorization. The project is driven by the potential of gamification to transform security education from a passive obligation into an active, immersive experience. By replacing static content with interactive challenges and immediate feedback, the system aims to build the “muscle memory” required for threat detection. The ultimate goal is to democratize digital safety, creating an accessible tool that empowers users of all backgroundsfrom students to confidently recognize and neutralize online threats. 2. Problem Statement In the rapidly digitizing landscape of India, a significant portion of the population particularly youth and non-technical users struggles to identify and respond to online threats in real- life situations. While technological security measures have advanced, studies indicate that the majority of successful cyberattacks are facilitated by human error and a lack of awareness rather than technical deficiencies. The core of the problem lies in the inadequacy of existing educational methodologies. Traditional cybersecurity training, often delivered through static lectures, text-heavy courses, or passive workshops, fails to capture learner attention or simulate the pressure of actual decision-making. This passive approach results in poor engagement and low knowledge retention, leaving users vulnerable to sophisticated attacks such as social engineering and phishing. Consequently, there is an urgent need for a smarter, more relatable, and engaging pedagogical approach that not only imparts theoretical knowledge but actively transforms how individuals think, respond, and act when facing digital threats. 3. Objectives The primary objective of this project is to develop an interactive Gamified Cybersecurity Awareness System that bridges the gap between theoretical knowledge and real-world application. The specific objectives are as follows: * To design an interactive web-based platform that utilizes gamification to enhance user engagement and motivation. * To integrate an intelligent Chatbot tutor that provides real- time guidance and corrective feedback during challenges. * To implement realistic simulations of critical threats, such as phishing and malware, for safe, hands-on practice. * To employ a structured pre-test and post-test framework to quantifiably measure knowledge retention and improvement. * To bridge the gap to real-world action by integrating a direct SOS link to CERT-In for reporting cyber incidents. 2. LITERATURE SURVEY Womens safety has emerged as a critical social and techno- logical concern due to the increasing number of harassments, assault, and violence-related incidents worldwide. Publicati on Year Sr no. | Paper Name | Advantages | Disadvantages | Technologies Used | Future Scope ---|---|---|---|---|--- 1 | Cyber-Hero: A Gamification Framework for Cyber Security Awareness for High School Students | May 2021 | Three-phase approach (Pre-test, Gamified Training, Post-test) ensures learning assessment and progress tracking. Works on any browser and operating system. Improves cybersecurity skills such as strong password creation. | Focuses mainly on password generation; other cyber threats are not covered. | Construct3 game development toolkit | Extend framework to include phishing, malware, plagiarism, and other cyber threats. 2 | A Novel SETA-Based Gamification Framework to Raise Cybersecurity Awareness | August 2021 | Covers multiple threats such as passwords, phishing, social engineering, and physical security. Simulates real workplace threats. Pre- and post-survey results show 51% improvement in awareness. | Basic design with limited graphics and interactivity. Tested on only 10 employees. | Python (PyCharm IDE), HTML, CSS, JavaScript, SQLite3 | Integrate chatbot for real-time feedback. Add advanced levels such as remote- work security and email security. 3 | Gamification- Based Cybersecurity Awareness Course for Self-Regulated Learning | April 2023 | Implemented on Moodle LMS. Structured learning ensures practice and reflection. Supports self-regulated learning environment. | Depends heavily on learner self- motivation. Gamification elements may lose effectiveness over time. | Moodle LMS and gamification plugins | Continuous topic updates for emerging cyber threats. Full-scale Moodle implementation. Sr no. | Paper Name | Publicati on Year | Advantages | Disadvantages | Technologies Used | Future Scope ---|---|---|---|---|---|--- 4 | AI-Powered Personalized Learning Platforms for Enhancing Cybersecurity Education | 2025 | Provides personalized learning paths. Improves motivation using adaptive gamification. Includes virtual labs and simulations. Highly scalable. | Privacy concerns due to sensitive data collection. Risk of AI bias. Requires strong internet connectivity and devices. | Artificial Intelligence, Machine Learning, NLP, Predictive Analytics, Adaptive Learning Systems | Apply deep learning for better personalization. Improve transparency and fairness in AI models. 5 | A Serious Game for Simulating Cyberattacks to Teach Cybersecurity | 2023 | Learners experience attacks as attackers, increasing engagement. Covers phishing, SQL injection, and USB attacks. Safe environment for practice. | No evidence of long- term learning. Limited scenarios. Complex 3D design may confuse non- gamers. | Nmap, Metasploit, SocialPhish; NIST Cybersecurity Framework | Add ransomware scenarios. Simplify visuals using 2D interfaces. 6 | CyberMoraba A Game- Based Approach Enhancing Cybersecurity Awareness | 2023 | Combines traditional African board game with cybersecurity concepts. Encourages strategy, competition, and interaction. Positive student feedback. | Tested only with students. Cultural impact not measured. Basic graphics and interaction. Desktop- dependent setup. | Morabaraba game logic, Visual Studio IDE, C#, GUI with attacker/defend er tokens | Introduce 3D levels and complex challenges. Use ML to analyze gameplay. Validate with professionals and diverse users. 7 | Learning Cyber Security Through Gamification | 2015 | Enhances student engagement through game-based learning. Provides practical cybersecurity training using real attack-defense scenarios. Improves problem-solving and hands- on security skills. | Limited long-term learning evaluation. Requires technical infrastructure setup. Mainly focused on competition environment. | MongoDB, Memcached, Flask Framework, Virtual Machines, Game Server | Integrate AI- driven adaptive learning, improve scalability, and include real- world cyberattack simulations. 8 | Gamification of Cybersecurity Awareness for Non-IT Professionals: A Systematic Literature Review | 2024 | Increases cybersecurity awareness among non-IT users. Improves motivation and participation using storytelling and leaderboards. Encourages behavioral change and better knowledge retention. Makes training more interactive than traditional methods. | Lack of long-term effectiveness analysis. Limited empirical validation. Individual game element impact not clearly measured | Learning Management Systems, Gamification Models, Behavioral Theories (SDT), Interactive Platforms | Develop personalized gamification strategies and conduct long- term engagement studies. 9 | Raising Cybersecurity Awareness in an Engaging Way Using Gamification and AI Tips | 2025 | Improves threat detection accuracy using AI nudging. Reduces risky online behavior among social media users. Provides real-time guidance for safer decisions. Enhances engagement through interactive gamified learning. | Requires continuous AI model updates. Engagement sustainability challenges. Legal awareness integration still developing. | AI Nudging System, NLP, TensorFlow Lite, Gamification Engine, Firebase, SQLite | Add personalization engine, multilingual support, offline access, and long- term behavioral analysis. Sr no. | Paper Name | Publicati on Year | Advantages | Disadvantages | Technologies Used | Future Scope ---|---|---|---|---|---|--- 10 | Cybersecurity Education Using Gamification: Systematic Literature Review | 2025 | Enhances learner engagement through interactive game elements such as points, badges, and leaderboards. Improves cybersecurity knowledge retention and motivation. Supports personalized and adaptive learning environments. | Over-reliance on rewards may reduce intrinsic motivation. Limited implementation in corporate training environments. Requires proper game design for effectiveness. Long- term behavioral impact still needs evaluation. | Web-based Platforms, Application- based Systems, Tabletop Games, PRISMA Methodology, Publish or Perish Tool, Scopus & Semantic Scholar Databases | Develope adaptive personalized gamification systems, expand usage in industry training, conduct long-term effectiveness studies, and improve scalable cybersecurity awareness platforms. 1. Limitations of Existing Systems Despite the growing popularity of gamified learning, current cybersecurity awareness platforms exhibit several critical limitations: * Limited Threat Coverage: Most existing systems focus narrowly on specific topics, such as password security or phishing, while neglecting broader and equally critical threats like malware, ransomware, and social engineering. * Simplistic Gamification: Many platforms rely solely on basic game mechanics like points and badges. hey lack advanced engagement features such as adaptive difficulty, real-time interactive challenges, or intelligent chatbot- based guidance, which reduces long-term user motivation. * Small-Scale Evaluations: A significant number of studies are validated on small groups (fewer than 30 participants), often limited to students or employees of a single organization. This makes it difficult to generalize their effectiveness to a wider, diverse population. * Platform Dependency: Many solutions are confined to specific environments like Moodle LMS or desktop applications. This restricts accessibility for users who prefer flexible, cross-platform access via mobile devices and web browsers. * Lack of Real-World Integration: Perhaps the most significant limitation is the disconnect between theory and action. Very few systems link awareness training to practical real-world outcomes, such as incident reporting mechanisms (e.g., CERT-In), limiting the translation of awareness into protective behavior. 3. METHODOLOGY 1. Proposed System Overview The proposed system, the Gamified Cybersecurity Awareness System, is an interactive web-based educational platform designed to provide active learning, real-time threat simulation, and intelligent guidance for users of all technical backgrounds. The system integrates Gamification mechanics, Natural Language Processing (NLP), Heuristic Analysis, and Procedural Generation to ensure engaging and effective cybersecurity training. Unlike conventional cybersecurity training methods that rely primarily on passive lectures and static text-based content, the proposed system supports multiple active learning modules such as algorithmic password strength analysis, collision-based phishing detection, and arcade-style malware defense. Upon encountering difficulty, an intelligent Chatbot Tutor provides real-time hints and corrective feedback, while a dedicated SOS feature allows users to immediately report actual incidents to national cybersecurity authorities (CERT-In). 2. System Architecture The overall architecture of the proposed system is illustrated in Fig. 1. The application adopts a modular client-server structure to ensure scalability and cross-platform accessibility. The system initializes by loading the user dashboard and activating the core game engine powered by the HTML5 Canvas and Web Audio API. User interactions are continuously processed by the backend logic, and based on the selected module, specific algorithms (such as Regex-based heuristics or Euclidean distance formulas) are executed. user interactions activate different system modules and how data flows between users, databases, and external authorities such as CERT-In. Fig. 1. System Architecture Gamified Cybersecurity Awareness System The architecture supports User Registration, Pre-Assessment, Interactive Challenge Execution (Password, Phishing, Malware), Chatbot Guidance, and Post-Assessment evaluation. The system is designed to operate seamlessly on standard web browsers, ensuring widespread accessibility without specialized hardware. 3. Data Flow Diagram To better understand system interactions, data flow diagrams (DFD) are used to represent information movement within the system. 1) Level 0 Data Flow Diagram: Fig. 2 illustrates the Level 0 DFD, which provides a high-level view of the interaction between the user, the safety application, emergency services, family members, and the legal database. Fig. 2. DFD Level 0 A System Development Approach Level 1 and Level 2 Data Flow Diagram: Fig. 3 and Fig. 4 illustrate the detailed functioning of the Gamified Cybersecurity Awareness System, including user management, challenge execution, gamified feedback, and incident reporting processes. These diagrams demonstrate how Fig. 3. DFD Level 1 Fig. 4. DFD Level 2 The development of the proposed Gamified Cybersecurity Awareness System follows the Software Development Life Cycle (SDLC) methodology to ensure system reliability, scalability, and effective performance. The major phases involved include requirement analysis, system design, implementation, testing, deployment, and maintenance. The system adopts a modular clientserver architecture in which components such as user authentication, interactive cybersecurity challenge execution, gamification and feedback management, chatbot-based guidance, and incident reporting operate independently while maintaining seamless coordination. This modular approach enhances system flexibility, enables efficient data processing, and supports future expansion for incorporating advanced cybersecurity scenarios and adaptive learning mechanisms. B. Operational Algorithm The operational flow of the proposed system is summarized as follows: 1. The system initializes core services including user authentication, database connectivity, and game engine modules. 2. The user registers or logs into the platform to access cybersecurity learning modules and assessments. 3. Based on user selection, the system loads interactive cybersecurity challenges such as phishing detection, password strength analysis, or malware awareness simulations. 4. User responses during challenges are continuously monitored and evaluated by the backend processing module. 5. The gamification engine calculates performance scores and generates rewards such as points, badges, and leaderboard rankings. 6. An intelligent chatbot module provides real-time guidance, hints, and corrective feedback to enhance user understanding. 7. User progress and performance data are stored in the database for analysis and future learning recommendations. 8. In case of real-world cyber incidents, users can report threats through the integrated CERT-In reporting feature. The system operates continuously during user interaction to ensure active learning, performance tracking, and effective cybersecurity awareness training. 4. RESULTS AND DISCUSSION The proposed Gamified Cybersecurity Awareness System was evaluated based on its functionality, user engagement, and learning effectiveness during interactive cybersecurity training sessions. The evaluation focused on key features such as gamified challenge execution, real-time feedback generation, performance assessment, and cybersecurity incident reporting integrationUser interaction analysis indicated improved engagement levels compared to traditional awareness methods. The platform efficiently tracked user progress through continuous performance monitoring. Additionally, the system maintained stable responsiveness during multiple user interactions, ensuring uninterrupted learning experiences. The overall evaluation confirmed the systems capability to enhance cybersecurity awareness and promote secure online behavior among users. 1. Functional Evaluation The system was tested on web-based platforms under different user interaction scenarios to evaluate its functionality and performance. The gamified learning modules successfully executed cybersecurity challenges such as phishing detection, password strength analysis, and malware awareness simulations. The system accurately evaluated user responses and generated real- time feedback along with scores, badges, and leaderboard updates. The chatbot module effectively provided guidance and corrective suggestions during challenges, improving user understanding. Additionally, user performance data was securely stored in the database, and the incident reportng feature enabled users to submit cybersecurity threats through the integrated CERT-In link, ensuring practical applicability of learned concepts. 2. Response Time Analysis Response time was measured from the moment a user initiated a cybersecurity challenge to the generation of system feedback and performance results. The system demonstrated minimal delay in loading interactive simulations and evaluating user responses under stable internet connectivity. Real-time feedback and score updates were generated efficiently, ensuring smooth user interaction during learning activities. The chatbot guidance module responded promptly to user queries, providing instant hints and corrective suggestions. Efficient backend processing and database communication reduced system latency, thereby enhancing overall user experience and maintaining continuous engagement throughout the training process. 3. AI-Based Chatbot Assistance Performance The AI and NLP-based chatbot module processed user queries related to cybersecurity threats and provided relevant guidance based on contextual understanding. The chatbot assisted users during interactive challenges by offering real-time hints, explanations, and corrective feedback for identified mistakes. This feature enhanced user learning by simplifying complex cybersecurity concepts and improving threat recognition skills. The intelligent assistance mechanism promoted better decision-making and increased user confidence in identifying and responding to potential cyber risks. 4. Comparative Discussion Compared to traditional cybersecurity awareness methods, the proposed system offers several improvements: * Interactive gamified learning instead of passive lecture-based training. * Real-time feedback and performance evaluation through game- based challenges. Direct cybersecurity incident reporting through CERT-In integration. These improvements contribute to enhanced user engagement, better knowledge retention, and respond to real-world cybersecurity threats. 5. Discussion The results indicate that integrating gamification techniques, artificial intelligence, and NLP-based chatbot assistance into a unified cybersecurity learning platform significantly enhances user engagement and learning effectiveness. The interactive challenge- based approach improves users ability to recognize and respond to common cyber threats in a practical environment. While the current implementation focuses on web-based deployment, further enhancements such as adaptive learning models and mobile platform integration can improve accessibility and scalability. Overall, the proposed Gamified Cybersecurity Awareness System demonstrates strong potential as an effective, intelligent, and user-centric solution for promoting cybersecurity awareness and safe online behavior. 5. CONCLUSION AND FUTURE WORK This paper presented the Gamified Cybersecurity Awareness System, which successfully achieves its objective of enhancing cybersecurity awareness through an interactive and engaging learning approach. The developed web-based platform provides an effective solution for educating users about cyber threats by incorporating gamified challenges, real-time feedback, and performance evaluation mechanisms. During system testing, the platform effectively simulated real-world cybersecurity scenarios such as phishing detection, password security analysis, and malware awareness training, enabling users to improve their threat recognition and decision-making skills. The user interface was designed to be simple, interactive, and accessible, ensuring usability for individuals with varying technical backgrounds. Furthermore, the inclusion of a CERT-In reporting feature bridges the gap between cybersecurity awareness and real- world action, making the system practical and impactful. Overall, the proposed system offers an intelligent, scalable, and user-centric solution for promoting safe digital behavior and strengthening cybersecurity awareness. Future work includes integrating adaptive learning algorithms for personalized training experiences, expanding cybersecurity scenarios to address emerging threats, developing mobile application support for improved accessibility, and incorporating advanced analytics to monitor long-term improvement in user cybersecurity behavior REFERENCE 1. C. Scherb, L. B. Heitz, F. Grimberg, H. Grieder, and M. Maurer, A serious game for simulating cyberattacks to teach cybersecurity, arXiv preprint arXiv:2305.03062, May 2023. 2. M. Nkongolo, CyberMoraba: A game-based approach enhancing cybersecurity awareness, University of Pretoria, Faculty of Informatics, South Africa, 2023. 3. W. Wayz, A. Rajuroy, and M. Ganz, AI-Powered Personalized Learning Platforms for Enhancing Cybersecurity Education, Jan. 2025. 4. F. Abu-Amara, R. Almansoori, S. Alharbi, M. Alharbi, and A. Alshehhi, A novel SETA-based gamification framework to raise cybersecurity awareness, International Journal of Information Technology, Aug. 2021. 5. K. Boopathi, S. Sreejith, and A. Bithin, Learning cyber security through gamification, Indian Journal of Science and Technology, vol. 8, no. 7, pp. 642649, Apr. 2015. 6. T. M. Tran, R. Beuran, and S. Hasegawa, Gamification-based cybersecurity awareness course for self-regulated learning, International Journal of Information and Education Technology, vol. 13, no. 4, Apr. 2023. 7. H. Qusa and J. Tarazi, Cyber-Her9o: A gamification framework for cyber security awareness for high school students, in Proc. IEEE International Conference on Computing and Communication, 2021. 8. T. Tan, R. S. Abdullah, and Z. Masud, Cybersecurity education using gamification: Systematic literature review, International Journal of Academic Research in Business and Social Sciences, vol. 15, no. 10, 2025. 9. A. K. Gwenhure and F. S. Rahayu, Gamification of cybersecurity awareness for non-IT professionals: A systematic literature review, International Journal of Serious Games, vol. 11, no. 1, Mar. 2024. 10. P. A. Gandhi, Raising cybersecurity awareness in an engaging way: How gamification and AI tips can help social media users stay safe from AI threats, International Journal of Scientific Research and Engineering Development, vol. 8, no. 3, MayJun. 2025. ______________

Gamified Cybersecurity Awareness System: An Interactive Approach to Digital Safety View Abstract & download full text of Gamified Cybersecurity Awareness System: An Interactive Approach to Digi...

#Volume #15, #Issue #03 #(March #2026)

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Real-time IoT-Based Women Safety and Emergency Response System **DOI :****https://doi.org/10.5281/zenodo.19388390** Download Full-Text PDF Cite this Publication Karthick D, Madheswari D, Prashanth R S, Mathumitha A, Sri Maheshwaran M, 2026, Real-time IoT-Based Women Safety and Emergency Response System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 3 * **Authors :** Karthick D, Madheswari D, Prashanth R S, Mathumitha A, Sri Maheshwaran M * **Paper ID :** IJERTV15IS031313 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 02-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Real-time IoT-Based Women Safety and Emergency Response System Karthick D Assistant Professor, Department of EEE Adithya Institute of Technology Coimbatore, India Madheswari D Assistant Professor, Department of EEE Adithya Institute of Technology Coimbatore, India Prashanth R S Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Mathumitha A Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Sri Maheshwaran M Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Abstract – Ensuring womens safety during emergency situations remains a critical societal challenge, particularly when immediate access to mobile devices is not feasible. This paper presents a Real-time IoT-Based Women Safety and Emergency Response System designed using an ESP32 microcontroller integrated with a NEO-6M GPS module and SIM800L GSM communication. The proposed system enables instant alert generation through a dedicated panic button, automatically transmitting the users real-time location to a cloud-based backend developed using the MERN (MongoDB, Express, React, Node.js) stack. Real-time communication is facilitated using Socket.IO, allowing alerts to be instantly visualized on role-based dashboards for administrators and responders. Additional features such as device registration, user authentication, and access control enhance system security and usability. Experimental evaluation demonstrates reliable GPS accuracy, minimal alert transmission delay, and stable dashboard responsiveness, validating the systems effectiveness for real-world deployment in campuses, workplaces, and public environments. Keywords Women safety, IoT alert system, GPS tracking, ESP32, SIM800L, Emergency response, Real-time monitoring, MERN stack. 1. INTRODUCTION Womens safety has become a significant concern due to the increasing number of incidents reported in public spaces, workplaces, and educational institutions. In many emergency situations, victims may not have sufficient time or physical ability to access their mobile phones to seek help. Mobile- based safety applications further depend on factors such as battery availability, user interaction, and network connectivity, which may not be reliable during critical moments. IoT-based wearable and hardware-assisted solutions provide an effective alternative by enabling rapid alert generation with minimal user involvement. Through the integration of GPS and GSM modules, IoT technologies support continuous monitoring and real-time data transmission. When combined with a cloud-based backend, emergency alerts can be received, processed, and displayed instantly for authorized responders. In the proposed Real-time IoT-Based Women Safety and Emergency Response System, an ESP32 microcontroller integrated with a NEO-6M GPS module and SIM800L GSM module is used to transmit the users real-time location to a cloud server upon activation of a panic button. The backend stores alert information and communicates with dashboards developed using React.js to display real-time tracking and alert notifications. The primary objective of the system is to provide a fast, reliable, and user-friendly emergency communication mechanism that functions effectively even without direct smartphone access. A. Need for the System Womens safety challenges continue to increase in both urban and rural environments due to delayed emergency communication, inadequate monitoring mechanisms, and the absence of real-time tracking solutions. During emergencies, users may be unable to unlock their phones, open applications, or contact emergency numbers, resulting in delayed assistance. Existing safety systems suffer from several limitations, including dependence on active mobile usage, lack of continuous real time tracking, absence of centralized monitoring authorities, and inconsistent alert delivery under poor network conditions. These limitations reduce the effectiveness of current solutions during critical situations. A dedicated hardware-based emergency alert system overcomes these challenges by enabling instant alert activation through a single panic button. GPS integration ensures accurate location tracking, while SIM800L-based GSM communication allows alerts to be transmitted even without Wi-Fi connectivity. Additionally, a centralized web-based dashboard enables authorities and responders to monitor alerts and take timely action. Hence, an IoT-based emergency alert system is essential to provide reliable, location-aware, and rapid support for women during distress situations. 2. PROBLEM STATEMENT Women often face unexpected and unsafe situations where conventional safety mechanisms fail due to fear, time constraints, or limited access to mobile devices. In emergency scenarios, performing multiple actions such as unlocking a phone, opening an application, and triggering an alert may not be practical. These delays can significantly affect the effectiveness of emergency response. Most existing safety solutions lack a dedicated hardware- based panic mechanism, automatic real-time location transmission, centralized monitoring, and structured role- based response management. The absence of these features results in delayed alerts, poor coordination among responders, and increased risk during critical situations. Therefore, there is a need for a system that provides a simple one-button alert mechanism, accurate real-time GPS tracking, instant cloud communication, centralized monitoring through dashboards, and a secure and scalable backend infrastructure to ensure timely and effective emergency response. 1. System Overview The proposed system consists of an IoT-based emergency alert device integrated with an ESP32 microcontroller, a NEO- 6M GPS module, and a SIM800L GSM module. When the panic button is pressed, the device captures the users current geographic coordinates and transmits the alert to a cloud- based backend server. The backend is developed using the MERN stack, where alert data is securely stored and processed. Real-time alert notifications are delivered to connected dashboards using Socket.IO, enabling administrators and authorized responders to view the users live location on a map. Additional features such as device registration, user authentication, and role-based access control ensure secure and organized emergency handling. The system workflow ensures that every alert is recorded, monitored, and resolved efficiently. 2. Functional Flow The functional operation of the proposed system follows a structured sequence to ensure reliable emergency alert generation and response. Device Activation and Location Capture: When the panic button is pressed, the ESP32 microcontroller is immediately activated. The device retrieves the latest valid GPS coordinates, including latitude, longitude, and timestamp, along with essential device information. The GPS module continuously monitors for a valid location fix to ensure accuracy. Alert Transmission: Once the alert data is prepared, the system establishes a communication link and transmits the alert to the backend server using GSM-based HTTP communication through the SIM800L module. In the event of temporary communication failure, the device retries transmission to ensure successful delivery. Server Processing and Real-time Broadcast: Upon receiving the alert, the backend server verifes the device identity, stores the alert details in the database, and assigns a unique alert identifier. The alert is then instantly broadcast to connected dashboards using Socket.IO, enabling real-time visibility for administrators and responders. Response and Alert Resolution: Authorized responders acknowledge the alert through the dashboard interface and monitor the users location updates in real-time. Once the situation is resolved, the alert status is updated and archived for future reference and analysis. 3. WORKING PRINCIPLE The proposed system operates by integrating IoT hardware with a cloud-connected backend to enable real-time emergency alerting and location tracking. The ESP32 microcontroller remains in an active monitoring state, continuously checking the status of the panic button while validating the availability of GPS coordinates. When the panic button is pressed, the ESP32 immediately retrieves the most recent latitude and longitude values from the NEO-6M GPS module. An alert packet is then generated, containing the device identifier, GPS coordinates, timestamp, and alert status. This information forms the basis of the emergency notification. The alert packet is transmitted to the backend server through the available communication channel. Wi-Fi is used when network connectivity is available, while the SIM800L GSM module serves as a reliable fallback by transmitting the alert using GSM communication in low or unstable internet conditions. This dual communication approach ensures dependable alert delivery. Upon receiving the alert, the backend server validates the registered device, stores the alert data in the database, and broadcasts a real-time notification to connected dashboards using Socket.IO. Administrators and responders can view the users location instantly on a map interface. The device continues to send periodic location updates until the alert is acknowledged and resolved, ensuring continuous monitoring throughout the emergency. 4. HARDWARE COMPONENTS The proposed system is built using reliable and cost- effective hardware components that collectively enable real- time emergency alert generation and communication. ESP32 Microcontroller: The ESP32 serves as the central processing unit of the system. It monitors the panic button, processes GPS data, manages Wi-Fi communication with the backend server, and coordinates alert transmission. NEO-6M GPS Module: The NEO-6M GPS module provides accurate real-time latitude and longitude information. It continuously updates the users location, which is included in both cloud alerts and emergency messages. SIM800L GSM Module: The SIM800L GSM module is used to send SOS alert messages containing the users location and map link to pre-registered emergency contacts. This ensures that immediate assistance from relatives or nearby helpers is possible even when internet connectivity is unavailable. Push Button: The push button functions as the emergency trigger. A single press activates the alert mechanism, minimizing the need for user interaction during distress situations. Power Supply Unit: A stable power supply is provided to support the ESP32, GPS, and GSM modules, ensuring uninterrupted system operation during emergencies. 5. Block diagram Fig. 1. Block diagram of the real-time IoT-based women safety and emergency response system Figure 1 shows the block diagram of the real-time IoT- based women safety and emergency response system. The ESP32 microcontroller acts as the central control unit of the system. A NEO-6M GPS module is interfaced with the ESP32 to acquire the users real-time geographical coordinates. A panic button connected to a digital input pin enables immediate activation during emergency situations. When the panic button is pressed, the ESP32 retrieves the latest GPS coordinates and prepares an alert data packet. The system continuously monitors the users location during the emergency to ensure updated tracking information. Wi-Fi communication is used to transmit the alert data to the cloud- based backend server, allowing real-time updates to be displayed on the monitoring dashboards. In parallel, the SIM800L GSM module sends an SOS message containing the users location along with a map link to pre- registered emergency contacts, enabling immediate assistance from relatives or nearby helpers even when internet connectivity is unavailable. Upon receiving the alert, the backend server verifies the device, stores the alert information, and updates the dashboards for users, responders, and administrators. This architecture ensures timely alert generation, continuous location monitoring, and prompt emergency response. 6. ADVANTAGES The proposed real-time IoT-based women safety and emergency response system offers several practical advantages over conventional safety mechanisms. Instant Emergency Alerting: The system enables users to trigger an emergency alert with a single panic button press, eliminating the need for mobile phone interaction during distress situations. Real-time Location Tracking: GPS-based tracking provides accurate and continuous location updates, allowing responders and emergency contacts to locate the user quickly. Dual Communication Support: Wi-Fi is used to transmit alert data to the cloud server, while the GSM module sends SOS messages with location map links to emergency contacts, ensuring alert delivery under varying network conditions. Role-Based Monitoring: Separate dashboards for administrators and responders improve situational awareness, coordination, and efficient emergency handling. Scalable Cloud Architecture: The cloud-based backend supports multiple devices and simultaneous alerts, making the system suitable for large-scale deployments. Low-Cost and Easy Deployment: The use of affordable and widely available hardware components makes the system economical and easy to implement. User-Friendly Operation: Minimal user interaction is required, as the entire emergency response process is initiated through a single button press. 7. RESULT AND DISCUSSION The performance of the proposed real-time IoT-based women safety and emergency response system was evaluated through experimental testing focusing on GPS accuracy, alert transmission delay, dashboard responsiveness, and overall system reliability. GPS Performance Evaluation: The NEO-6M GPS module was tested in different environments to measure location accuracy and lock time. In open areas, the system achieved faster GPS lock times with higher accuracy, while in semi-urban and indoor conditions, slightly increased lock times were observed. Despite this, the obtained accuracy remained sufficient for emergency tracking applications, ensuring reliable location identification during distress situations. Alert Transmission Delay Analysis: Alert transmission performance was analyzed under varying network conditions. When Wi-Fi connectivity was available, alert data was transmitted to the backend server with minimal delay. The GSM module successfully delivered SOS messages containing location map links to emergency contacts even under weak signal conditions. The observed transmission delays remained within acceptable limits for emergency response scenarios. Dashboard Responsiveness: The user, responder, and administrator dashboards were tested under continuous alert conditions. Real-time updates using Socket.IO demonstrated minimal latency, allowing alerts and location updates to be displayed almost instantly. Alert acknowledgment and resolution actions were reflected consistently across all dashboards. System Stability and Reliability: The system was subjected to repeated alert generation and extended operation tests. No data loss was observed, and stable communication was maintaind throughout testing. The combined use of Wi- Fi-based server communication and GSM-based SOS messaging enhanced system reliability across different network environments. Overall, the experimental results confirm that the proposed system delivers reliable performance, timely alert transmission, and stable real-time monitoring, making it suitable for deployment in real-world safety-critical environments. 8. CONCLUSION This paper presented a real-time IoT-based women safety and emergency response system designed to provide quick and reliable assistance during critical situations. By integrating an ESP32 microcontroller with GPS and GSM communication modules, the system enables accurate location tracking and immediate alert generation through a single panic button press. Wi-Fi communication ensures real-time data transmission to the cloud server, while GSM-based SOS messaging delivers location details to emergency contacts when internet connectivity is unavailable. The cloud-based backend and role-based dashboards allow administrators and responders to monitor alerts and track user locations efficiently. Experimental evaluation demonstrated low alert transmission delay, acceptable GPS accuracy, and stable real time dashboard performance. Due to its low cost, scalability, and ease of deployment, the proposed system is suitable for use in educational institutions, workplaces, and public environments. Future enhancements may include mobile application integration, geofencing-based alerts, and advanced analytics to further improve system effectiveness. ACKNOWLEDGMENT The authors would like to express their sincere gratitude to Dr. B. Padmanabhan, Head of the Department of Electrical and Electronics Engineering, Adithya Institute of Technology, for his continuous support, guidance, and encouragement throughout the course of this project. His valuable suggestions and motivation played a crucial role in the successful completion of this work. The authors also extend their appreciation to the department faculty members, laboratory staff, and technical assistants for their timely support during the hardware implementation and testing phases. Special thanks are extended to classmates and friends who contributed directly or indirectly to the successful execution of this project. REFERENCES 1. P. Sravani, B. Sindhu, N. Shashank, P. Pranaya, and S. Tejaswi, Women safety system using GPS and GSM tracking, International Journal of Engineering and Science Research (IJESR), vol. 15, no. 2, 2025. 2. S. R. Abhiraj, A. Nizar, A. Vijay, S. Rahna, and G. S. Anju, A comprehensive IoT-based unified approach for women safety alerts using GSM, International Journal of Recent Advances in Multidisciplinary Topics (IJRAMT), vol. 5, no. 5, May 2024. 3. C. M. Maind, C. Jalgi, M. Jadhav, and G. Maniyar, Women safety device with GPS tracking and SMS alerts, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2025. 4. H. Wankhade, S. Mahajan, and S. P. Gopnarayan, Womens safety device with GPS tracking and alert, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022. 5. D. N. Sisanalli, Women safety device with GPS tracking and alerts, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2024. 6. P. Preeti and M. Tajammul, Smart women protection system using IoT, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022. 7. Nivedetha B., C. Architha, and A. Aishwarya, Wearable device for women safety using IoT, in EAI/Springer Innovations in Communication and Computing (ICCAP 2021), Springer, 2022. ______________

Real-time IoT-Based Women Safety and Emergency Response System View Abstract & download full text of Real-time IoT-Based Women Safety and Emergency Response System Download Full-Text PDF Cite t...

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Real-time IoT-Based Women Safety and Emergency Response System **DOI :****https://doi.org/10.5281/zenodo.19388390** Download Full-Text PDF Cite this Publication Karthick D, Madheswari D, Prashanth R S, Mathumitha A, Sri Maheshwaran M, 2026, Real-time IoT-Based Women Safety and Emergency Response System, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 3 * **Authors :** Karthick D, Madheswari D, Prashanth R S, Mathumitha A, Sri Maheshwaran M * **Paper ID :** IJERTV15IS031313 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 02-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Real-time IoT-Based Women Safety and Emergency Response System Karthick D Assistant Professor, Department of EEE Adithya Institute of Technology Coimbatore, India Madheswari D Assistant Professor, Department of EEE Adithya Institute of Technology Coimbatore, India Prashanth R S Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Mathumitha A Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Sri Maheshwaran M Final Year Student, Department of EEE Adithya Institute of Technology Coimbatore, India Abstract – Ensuring womens safety during emergency situations remains a critical societal challenge, particularly when immediate access to mobile devices is not feasible. This paper presents a Real-time IoT-Based Women Safety and Emergency Response System designed using an ESP32 microcontroller integrated with a NEO-6M GPS module and SIM800L GSM communication. The proposed system enables instant alert generation through a dedicated panic button, automatically transmitting the users real-time location to a cloud-based backend developed using the MERN (MongoDB, Express, React, Node.js) stack. Real-time communication is facilitated using Socket.IO, allowing alerts to be instantly visualized on role-based dashboards for administrators and responders. Additional features such as device registration, user authentication, and access control enhance system security and usability. Experimental evaluation demonstrates reliable GPS accuracy, minimal alert transmission delay, and stable dashboard responsiveness, validating the systems effectiveness for real-world deployment in campuses, workplaces, and public environments. Keywords Women safety, IoT alert system, GPS tracking, ESP32, SIM800L, Emergency response, Real-time monitoring, MERN stack. 1. INTRODUCTION Womens safety has become a significant concern due to the increasing number of incidents reported in public spaces, workplaces, and educational institutions. In many emergency situations, victims may not have sufficient time or physical ability to access their mobile phones to seek help. Mobile- based safety applications further depend on factors such as battery availability, user interaction, and network connectivity, which may not be reliable during critical moments. IoT-based wearable and hardware-assisted solutions provide an effective alternative by enabling rapid alert generation with minimal user involvement. Through the integration of GPS and GSM modules, IoT technologies support continuous monitoring and real-time data transmission. When combined with a cloud-based backend, emergency alerts can be received, processed, and displayed instantly for authorized responders. In the proposed Real-time IoT-Based Women Safety and Emergency Response System, an ESP32 microcontroller integrated with a NEO-6M GPS module and SIM800L GSM module is used to transmit the users real-time location to a cloud server upon activation of a panic button. The backend stores alert information and communicates with dashboards developed using React.js to display real-time tracking and alert notifications. The primary objective of the system is to provide a fast, reliable, and user-friendly emergency communication mechanism that functions effectively even without direct smartphone access. A. Need for the System Womens safety challenges continue to increase in both urban and rural environments due to delayed emergency communication, inadequate monitoring mechanisms, and the absence of real-time tracking solutions. During emergencies, users may be unable to unlock their phones, open applications, or contact emergency numbers, resulting in delayed assistance. Existing safety systems suffer from several limitations, including dependence on active mobile usage, lack of continuous real time tracking, absence of centralized monitoring authorities, and inconsistent alert delivery under poor network conditions. These limitations reduce the effectiveness of current solutions during critical situations. A dedicated hardware-based emergency alert system overcomes these challenges by enabling instant alert activation through a single panic button. GPS integration ensures accurate location tracking, while SIM800L-based GSM communication allows alerts to be transmitted even without Wi-Fi connectivity. Additionally, a centralized web-based dashboard enables authorities and responders to monitor alerts and take timely action. Hence, an IoT-based emergency alert system is essential to provide reliable, location-aware, and rapid support for women during distress situations. 2. PROBLEM STATEMENT Women often face unexpected and unsafe situations where conventional safety mechanisms fail due to fear, time constraints, or limited access to mobile devices. In emergency scenarios, performing multiple actions such as unlocking a phone, opening an application, and triggering an alert may not be practical. These delays can significantly affect the effectiveness of emergency response. Most existing safety solutions lack a dedicated hardware- based panic mechanism, automatic real-time location transmission, centralized monitoring, and structured role- based response management. The absence of these features results in delayed alerts, poor coordination among responders, and increased risk during critical situations. Therefore, there is a need for a system that provides a simple one-button alert mechanism, accurate real-time GPS tracking, instant cloud communication, centralized monitoring through dashboards, and a secure and scalable backend infrastructure to ensure timely and effective emergency response. 1. System Overview The proposed system consists of an IoT-based emergency alert device integrated with an ESP32 microcontroller, a NEO- 6M GPS module, and a SIM800L GSM module. When the panic button is pressed, the device captures the users current geographic coordinates and transmits the alert to a cloud- based backend server. The backend is developed using the MERN stack, where alert data is securely stored and processed. Real-time alert notifications are delivered to connected dashboards using Socket.IO, enabling administrators and authorized responders to view the users live location on a map. Additional features such as device registration, user authentication, and role-based access control ensure secure and organized emergency handling. The system workflow ensures that every alert is recorded, monitored, and resolved efficiently. 2. Functional Flow The functional operation of the proposed system follows a structured sequence to ensure reliable emergency alert generation and response. Device Activation and Location Capture: When the panic button is pressed, the ESP32 microcontroller is immediately activated. The device retrieves the latest valid GPS coordinates, including latitude, longitude, and timestamp, along with essential device information. The GPS module continuously monitors for a valid location fix to ensure accuracy. Alert Transmission: Once the alert data is prepared, the system establishes a communication link and transmits the alert to the backend server using GSM-based HTTP communication through the SIM800L module. In the event of temporary communication failure, the device retries transmission to ensure successful delivery. Server Processing and Real-time Broadcast: Upon receiving the alert, the backend server verifes the device identity, stores the alert details in the database, and assigns a unique alert identifier. The alert is then instantly broadcast to connected dashboards using Socket.IO, enabling real-time visibility for administrators and responders. Response and Alert Resolution: Authorized responders acknowledge the alert through the dashboard interface and monitor the users location updates in real-time. Once the situation is resolved, the alert status is updated and archived for future reference and analysis. 3. WORKING PRINCIPLE The proposed system operates by integrating IoT hardware with a cloud-connected backend to enable real-time emergency alerting and location tracking. The ESP32 microcontroller remains in an active monitoring state, continuously checking the status of the panic button while validating the availability of GPS coordinates. When the panic button is pressed, the ESP32 immediately retrieves the most recent latitude and longitude values from the NEO-6M GPS module. An alert packet is then generated, containing the device identifier, GPS coordinates, timestamp, and alert status. This information forms the basis of the emergency notification. The alert packet is transmitted to the backend server through the available communication channel. Wi-Fi is used when network connectivity is available, while the SIM800L GSM module serves as a reliable fallback by transmitting the alert using GSM communication in low or unstable internet conditions. This dual communication approach ensures dependable alert delivery. Upon receiving the alert, the backend server validates the registered device, stores the alert data in the database, and broadcasts a real-time notification to connected dashboards using Socket.IO. Administrators and responders can view the users location instantly on a map interface. The device continues to send periodic location updates until the alert is acknowledged and resolved, ensuring continuous monitoring throughout the emergency. 4. HARDWARE COMPONENTS The proposed system is built using reliable and cost- effective hardware components that collectively enable real- time emergency alert generation and communication. ESP32 Microcontroller: The ESP32 serves as the central processing unit of the system. It monitors the panic button, processes GPS data, manages Wi-Fi communication with the backend server, and coordinates alert transmission. NEO-6M GPS Module: The NEO-6M GPS module provides accurate real-time latitude and longitude information. It continuously updates the users location, which is included in both cloud alerts and emergency messages. SIM800L GSM Module: The SIM800L GSM module is used to send SOS alert messages containing the users location and map link to pre-registered emergency contacts. This ensures that immediate assistance from relatives or nearby helpers is possible even when internet connectivity is unavailable. Push Button: The push button functions as the emergency trigger. A single press activates the alert mechanism, minimizing the need for user interaction during distress situations. Power Supply Unit: A stable power supply is provided to support the ESP32, GPS, and GSM modules, ensuring uninterrupted system operation during emergencies. 5. Block diagram Fig. 1. Block diagram of the real-time IoT-based women safety and emergency response system Figure 1 shows the block diagram of the real-time IoT- based women safety and emergency response system. The ESP32 microcontroller acts as the central control unit of the system. A NEO-6M GPS module is interfaced with the ESP32 to acquire the users real-time geographical coordinates. A panic button connected to a digital input pin enables immediate activation during emergency situations. When the panic button is pressed, the ESP32 retrieves the latest GPS coordinates and prepares an alert data packet. The system continuously monitors the users location during the emergency to ensure updated tracking information. Wi-Fi communication is used to transmit the alert data to the cloud- based backend server, allowing real-time updates to be displayed on the monitoring dashboards. In parallel, the SIM800L GSM module sends an SOS message containing the users location along with a map link to pre- registered emergency contacts, enabling immediate assistance from relatives or nearby helpers even when internet connectivity is unavailable. Upon receiving the alert, the backend server verifies the device, stores the alert information, and updates the dashboards for users, responders, and administrators. This architecture ensures timely alert generation, continuous location monitoring, and prompt emergency response. 6. ADVANTAGES The proposed real-time IoT-based women safety and emergency response system offers several practical advantages over conventional safety mechanisms. Instant Emergency Alerting: The system enables users to trigger an emergency alert with a single panic button press, eliminating the need for mobile phone interaction during distress situations. Real-time Location Tracking: GPS-based tracking provides accurate and continuous location updates, allowing responders and emergency contacts to locate the user quickly. Dual Communication Support: Wi-Fi is used to transmit alert data to the cloud server, while the GSM module sends SOS messages with location map links to emergency contacts, ensuring alert delivery under varying network conditions. Role-Based Monitoring: Separate dashboards for administrators and responders improve situational awareness, coordination, and efficient emergency handling. Scalable Cloud Architecture: The cloud-based backend supports multiple devices and simultaneous alerts, making the system suitable for large-scale deployments. Low-Cost and Easy Deployment: The use of affordable and widely available hardware components makes the system economical and easy to implement. User-Friendly Operation: Minimal user interaction is required, as the entire emergency response process is initiated through a single button press. 7. RESULT AND DISCUSSION The performance of the proposed real-time IoT-based women safety and emergency response system was evaluated through experimental testing focusing on GPS accuracy, alert transmission delay, dashboard responsiveness, and overall system reliability. GPS Performance Evaluation: The NEO-6M GPS module was tested in different environments to measure location accuracy and lock time. In open areas, the system achieved faster GPS lock times with higher accuracy, while in semi-urban and indoor conditions, slightly increased lock times were observed. Despite this, the obtained accuracy remained sufficient for emergency tracking applications, ensuring reliable location identification during distress situations. Alert Transmission Delay Analysis: Alert transmission performance was analyzed under varying network conditions. When Wi-Fi connectivity was available, alert data was transmitted to the backend server with minimal delay. The GSM module successfully delivered SOS messages containing location map links to emergency contacts even under weak signal conditions. The observed transmission delays remained within acceptable limits for emergency response scenarios. Dashboard Responsiveness: The user, responder, and administrator dashboards were tested under continuous alert conditions. Real-time updates using Socket.IO demonstrated minimal latency, allowing alerts and location updates to be displayed almost instantly. Alert acknowledgment and resolution actions were reflected consistently across all dashboards. System Stability and Reliability: The system was subjected to repeated alert generation and extended operation tests. No data loss was observed, and stable communication was maintaind throughout testing. The combined use of Wi- Fi-based server communication and GSM-based SOS messaging enhanced system reliability across different network environments. Overall, the experimental results confirm that the proposed system delivers reliable performance, timely alert transmission, and stable real-time monitoring, making it suitable for deployment in real-world safety-critical environments. 8. CONCLUSION This paper presented a real-time IoT-based women safety and emergency response system designed to provide quick and reliable assistance during critical situations. By integrating an ESP32 microcontroller with GPS and GSM communication modules, the system enables accurate location tracking and immediate alert generation through a single panic button press. Wi-Fi communication ensures real-time data transmission to the cloud server, while GSM-based SOS messaging delivers location details to emergency contacts when internet connectivity is unavailable. The cloud-based backend and role-based dashboards allow administrators and responders to monitor alerts and track user locations efficiently. Experimental evaluation demonstrated low alert transmission delay, acceptable GPS accuracy, and stable real time dashboard performance. Due to its low cost, scalability, and ease of deployment, the proposed system is suitable for use in educational institutions, workplaces, and public environments. Future enhancements may include mobile application integration, geofencing-based alerts, and advanced analytics to further improve system effectiveness. ACKNOWLEDGMENT The authors would like to express their sincere gratitude to Dr. B. Padmanabhan, Head of the Department of Electrical and Electronics Engineering, Adithya Institute of Technology, for his continuous support, guidance, and encouragement throughout the course of this project. His valuable suggestions and motivation played a crucial role in the successful completion of this work. The authors also extend their appreciation to the department faculty members, laboratory staff, and technical assistants for their timely support during the hardware implementation and testing phases. Special thanks are extended to classmates and friends who contributed directly or indirectly to the successful execution of this project. REFERENCES 1. P. Sravani, B. Sindhu, N. Shashank, P. Pranaya, and S. Tejaswi, Women safety system using GPS and GSM tracking, International Journal of Engineering and Science Research (IJESR), vol. 15, no. 2, 2025. 2. S. R. Abhiraj, A. Nizar, A. Vijay, S. Rahna, and G. S. Anju, A comprehensive IoT-based unified approach for women safety alerts using GSM, International Journal of Recent Advances in Multidisciplinary Topics (IJRAMT), vol. 5, no. 5, May 2024. 3. C. M. Maind, C. Jalgi, M. Jadhav, and G. Maniyar, Women safety device with GPS tracking and SMS alerts, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2025. 4. H. Wankhade, S. Mahajan, and S. P. Gopnarayan, Womens safety device with GPS tracking and alert, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022. 5. D. N. Sisanalli, Women safety device with GPS tracking and alerts, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2024. 6. P. Preeti and M. Tajammul, Smart women protection system using IoT, Journal for Research in Applied Science and Engineering Technology (IJRASET), 2022. 7. Nivedetha B., C. Architha, and A. Aishwarya, Wearable device for women safety using IoT, in EAI/Springer Innovations in Communication and Computing (ICCAP 2021), Springer, 2022. ______________

Real-time IoT-Based Women Safety and Emergency Response System View Abstract & download full text of Real-time IoT-Based Women Safety and Emergency Response System Download Full-Text PDF Cite t...

#Volume #15, #Issue #03 #(March #2026)

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AVA: AI Veterinary Assistance – An NLP and Semantic Vector-Based Clinical Decision Support System for Animal Healthcare **DOI :****https://doi.org/10.5281/zenodo.19369783** Download Full-Text PDF Cite this Publication Mr. Alen Denny, Mr. Christin V S, Mr. Basil Paul, Ms. Sheelu Susan Mathews, Mr. Christo Tomy Joseph, 2026, AVA: AI Veterinary Assistance – An NLP and Semantic Vector-Based Clinical Decision Support System for Animal Healthcare, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 2 * **Authors :** Mr. Alen Denny, Mr. Christin V S, Mr. Basil Paul, Ms. Sheelu Susan Mathews, Mr. Christo Tomy Joseph * **Paper ID :** IJERTV15IS031017 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 01-04-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### AVA: AI Veterinary Assistance – An NLP and Semantic Vector-Based Clinical Decision Support System for Animal Healthcare Mr. Alen Denny Department of Computer Science & Engineering Federal Institute of Science and Technology Angamaly, India Mr. Christin V S Department of Computer Science & Engineering Federal Institute of Science and Technology Angamaly, India Mr. Basil Paul Department of Computer Science & Engineering Federal Institute of Science and Technology Angamaly, India Ms. Sheelu Susan Mathews Assistant Professor (Guide) Department of Computer Science & Engineering Federal Institute of Science and Technology Angamaly, India Mr. Christo Tomy Joseph Department of Computer Science & Engineering Federal Institute of Science and Technology Angamaly, India Abstract Quick and correct veterinary assessment is a major problem for pet owners who often nd it hard to tell the difference between minor health issues and dangerous emergencies. Traditional symptom checking methods depend on hospital-based clinical visits, which take a lot of time, need specic institutions, and are not available outside working hours. This paper presents AVA (AI Veterinary Assistance), a smart, NLP-based clinical decision support system for basic veterinary assessment. The system handles unstructured natural language symptom descriptions using a two-part prediction design that includes a Lexical Heuristic Matcher and a Semantic Vector Engine built on the all-MiniLM-L6-v2 SentenceTransformer model. AVA pulls out structured patient proles from free-form text, links symptoms to a carefully collected MongoDB disease database of over 205 conditions, creates relevant follow-up questions, and provides ranked possible diagnoses with condence scores and urgency levels. Testing results show a macro-average AUC of 0.988 and strong disease classication performance across multiple veterinary categories. The system is built as an interactive Streamlit web application with multi-language support, voice input through Whisper ASR, and optional skin lesion image analysis. AVA offers a scalable, easy-to-use, and clear AI-powered framework for helping pet owners and veterinary professionals in basic clinical assessment. Keywords Articial Intelligence; Natural Language Processing; Veterinary Decision Support; Semantic Embeddings; Clinical Triage; Disease Prediction; SentenceTransformers; Streamlit. 1. INTRODUCTION Veterinary care depends heavily on quick and correct identica- tion of clinical signs. Pet owners are increasingly the rst ones to assess animal health, yet they lack clinical training to tell the difference between conditions that need immediate emergency attention and those that can be handled with home care. Delays or wrong understanding in this basic assessment phase can badly affect patient outcomes [1]. The fast growth of AI and natural language processing (NLP) in medical and clinical elds has created new chances for smart decision support systems. While big improvements have been made in human healthcare applications [2, 7], veterinary medicine has received much less attention in the clinical AI research. Existing tools for pet owners either depend on very basic keyword matching or provide general, often worrying in- formation taken from common search engines, neither of which is good enough for organized clinical assessment. This paper introduces AVA (AI Veterinary Assistance), a spe- cialized clinical decision support system made to ll this gap. AVA takes unstructured text or voice descriptions of animal symptoms, pulls out structured clinical proles using a multi- step NLP pipeline, and provides ranked possible diagnoses with urgency levels and specic follow-up questions. The system combines a rule-based Lexical Heuristic Engine with a dense Semantic Vector Engine, getting strong performance even when user descriptions use informal or unclear medical language. The main contributions of this work are: 1. A two-part disease prediction design combining heuristic and semantic approaches; 2. A structured NLP extraction pipeline for veterinary clinical attributes from free-form text; 3. An adaptive follow-up question generation module that im- proves diagnostic condence; 4. A carefully collected MongoDB-backed veterinary disease database covering 205+ conditions; 5. A ready-to-use, multi-language web interface with voice and image input support; 6. Experimental testing showing macro-average AUC of 0.988 on test data. The rest of this paper is organized as follows: Section II gives background and related work; Section III describes the sys- tem methodology; Section IV details the system architecture and modules; Section V presents algorithms; Section VI cov- ers implementation; Section VII reports experimental results; Section VIII discusses challenges and limitations; Section IX concludes with future research directions. 2. BACKGROUND AND RELATED WORK 1. 1. AI in Clinical Decision Support Clinical decision support systems (CDSS) have changed a lot with improvements in machine learning and NLP. Early rule- based expert systems have been replaced by data-driven models that can handle unstructured clinical text. Large language mod- els (LLMs) such as BERT [7] and GPT-4o [6] have shown strong performance on clinical information extraction tasks, and few-shot prompting methods have further improved structured extraction from limited medical records [2]. Image-based diagnostic AI has also grown, with deep learning models achieving high accuracy in radiology [3] and histopathol- ogy tasks. Audio-based diagnostic tools, such as lung sound classiers using one-dimensional convolutional neural networks (1D-CNNs), have achieved over 90% accuracy in respiratory condition detection [4]. Disease onset prediction models using electronic health records have achieved around 85% reliability in future population health settings [5]. 2. Veterinary AI Veterinary AI research has focused mainly on image-based diag- nostics, with deep learning frameworks achieving F1 scores of up to 0.88 in multi-class animal disease classication tasks [1]. However, NLP-based veterinary assessment systems that accept free-form natural language input remain rare. The challenges specic to veterinary NLP include the variety of species-specic symptom vocabulary, informal owner descriptions, and the lack of large labelled datasets similar to those available in human medicine. 3. Semantic Embedding-Based Retrieval Dense vector retrieval using sentence-level embeddings has proven useful for clinical information retrieval tasks. The all-MiniLM-L6-v2 model from the SentenceTransform- ers library produces 384-dimensional embeddings that balance computational efciency with strong semantic similarity per- formance [8]. Cosine similarity over pre-computed disease embeddings allows real-time matching even on basic hardware, making it suitable for deployment in settings with limited com- putational resources. 3. METHODOLOGY AVA uses a modular, pipeline-based methodology designed to ensure clear separation between input processing, clinical reasoning, and output generation. 1. Literature and Knowledge Base ConstructionVeterinary disease proles were collected from peer-reviewed veterinary references, clinical databases, and domain expert con- sultation. Each disease record includes species compatibility ags, symptom lists, severity classication, treatment recom- mendations, and prevention guidelines. The resulting MongoDB collection contains 205+ disease entries covering gastrointesti- nal, respiratory, dermatological, neurological, urinary, and sys- temic categories across dogs, cats, and bovine species. 2. NLP Extraction Pipeline Raw user input (text or transcribed audio) is processed through a multi-step NLP pipeline built using spaCy and NLTK. The pipeline performs tokenization, lemmatization, and pattern- based extraction across 30+ veterinary symptom categories. Pa- tient demographic attributes (species, age, breed, weight) are extracted using specic regular expressions. Negation detection and contextual ltering are applied to reduce wrong symptom assignments. 3. Dual-Engine Prediction AVA uses two complementary prediction engines working in parallel: Lexical Heuristic Engine: Calculates a baseline condence score for each candidate disease by computing the ratio of matched symptoms to total known disease symptoms. Cate- gorical boosters (+0.03 per matched category) and species lters are applied to adjust scores. Semantic Vector Engine: Encodes the patient symptom prole as a 384-dimensional embedding using all-MiniLM-L6-v2. Cosine similarity is calculated against pre-embedded disease vectors stored in MongoDB. A combined score mixing semantic similarity (75% weight) and lexical overlap (25% weight) is calculated and normalized. 4. Adaptive Follow-Up Generation The top-ranked candidate diseases are passed to the Follow-Up Question Generator, which nds missing information in the initial patient description and creates 68 targeted clarifying questions organized across three categories: Symptom Details, Medical History, and Lifestyle. Questions are prioritized based on the severity of the leading possible diagnosis. 5. Evaluation Protocol The system was tested on a held-out dataset of veterinary case descriptions labeled with ground-truth disease categories and severity levels. Standard classication metrics were calculated: accuracy, weighted F1-score, and multiclass area under the ROC curve (AUC). Bayesian hyperparameter optimization was ap- plied to calibrate the condence threshold (best C = 0.003594). 4. SYSTEM ARCHITECTURE Figure 1: Overall System Architecture of AVA The AVA system architecture is organized into three main pro- cessing stages, as shown in Figure 1. 1. 1. Preprocessing Stage The preprocessing stage takes raw multi-modal input (text, au- dio, or image). Text is cleaned and normalized; audio is tran- scribed using the Whisper ASR model; uploaded skin images undergo quality validation. Demographic attributes (species, age, breed, weight) are extracted and structured for further pro- cessing. 2. Analysis and Processing Stage The main processing stage includes ve sequential components: * VeterinaryNLPAnalyzer: Rule-based symptom extraction over 30+ categories. * MongoDiseaseRepository: Disease matching and ranking against the collected database. * Lexical Prediction: Heuristic scoring and categorical con- dence boosters. * Semantic Vector Engine: SentenceTransformer-based em- bedding similarity computation. * Condence Calculation: Bayesian score fusion and urgency classication. 3. Validation and Output Stage The validation stage applies score ltering, creates the adaptive follow-up question set, and puts together the nal structured JSON clinical report containing ranked disease candidates, con- dence scores, urgency assessment, and treatment recommenda- tions for display on the Streamlit interface. 5. MODULE DESCRIPTIONS 1. NLP Patient Analyzer Module The NLP Patient Analyzer handles unstructured text describ- ing animal health issues. It extracts demographic variables (animal type, age, breed, weight) using named-entity recog- nition patterns and regular expressions. Symptom extraction uses deep pattern matching across 30+ veterinary symptom cat- egories including gastrointestinal, respiratory, dermatological, neurological, and urinary signs. Complex symptom attributes duration, severity, and frequency are captured through con- textual phrase analysis, and surrounding context is ltered to resolve unclear clinical signs. 2. Lexical Prediction Module The Lexical Heuristic Matcher serves as the main fast-scoring engine. It queries the MongoDB disease collection ltered by the target species and calculates a baseline condence score as the ratio of matched to total known disease symptoms. Cate- gorical condence boosters are applied for symptom-category alignment (e.g., +0.03 when a urination abnormality maps to the urinary category). Conditions falling below a pruning threshold are removed, and the remaining candidates are returned with calculated heuristic condence values. 3. Semantic Vector Engine Module The Semantic Vector Engine works as a separate micro-service. During ofine pre-computation, disease text proles (name, description, symptom list) are encoded into 384-dimensional dense vectors using all-MiniLM-L6-v2 and stored in the MongoDB diseases vector collection. During inference, the patient symptom text is encoded using the same model, cosine similarity is calculated against all stored disease vectors, scores are adjusted by a 25% lexical overlap component, and a normalized semantic ranking matrix is returned. A smooth fallback to the lexical engine is provided if the vector service is unavailable. 4. Follow-Up Question Generator Module The Follow-Up Question Generator nds missing clinical details from the initial patient description and smartly produces 68 contextual follow-up questions. Questions are organized into three categories Symptom Details, Medical History, and Lifestyle and are prioritized based on the severity of the top- ranked possible diagnosis. Answers to follow-up questions are added into a rened condence update cycle, greatly improving overall clinical report accuracy. 6. ALGORITHMS 1. Main System Workow 1. Accept raw unstructured text or audio input. 2. Pass input to NLP Analyzer to clean and extract patient attributes and symptoms. 3. Run base heuristic scoring through the Lexical Prediction Engine. 4. Perform semantic matching via the Semantic Vector Engine. 5. Receive condence scores and ranked candidate diseases. 6. Send leading disease candidates to the Follow-Up Question Generator. 7. Compile and output the structured clinical JSON report to the Streamlit UI. 2. Lexical Heuristic Scoring 1. Get target species and the NLP-extracted symptom set. 2. Query the MongoDB diseases collection for the matching animal prole. 3. Calculate baseline score: (matched symptoms) / (total dis- ease symptoms). 4. Apply categorical condence boosters per symptom-category alignment. 5. Remove conditions that fail the condence threshold. 6. Return ranked array of conditions with heuristic condence values. 3. Semantic Vector Engine Ofine pre-computation: 1. Extract text proles (name, description, symptoms) for each disease. 2. Encode into 384-dimensional vectors via all-MiniLM-L6-v2. 3. Store vector arrays into the diseases vector MongoDB collection. Real-time prediction: 1. Receive live patient symptom text. 2. Transform text using the same SentenceTransformer model. 3. Calculate cosine similarity against all stored disease vectors. 4. Adjust scores via localized lexical overlap (25% weight). 5. Return normalized semantic ranking matrix. 7. IMPLEMENTATION DETAILS A. Technology Stack Table 1: AVA Technology Stack pre-computing and caching disease embeddings in MongoDB rather than recalculating during inference. Multi-language sup- port required integration of a translation layer to normalize non-English inputs before NLP processing. Whisper ASR inte- gration added latency for voice inputs, partially xed through chunked audio processing. Regulatory and privacy considera- tions led to the design decision to process all data locally without sending patient information to external APIs. 8. PERFORMANCE EVALUATION 1. Evaluation Metrics System performance was measured using standard multi-class classication metrics calculated on the verified diseases vector held-out test set. Met- rics include per-class precision, recall, weighted F1-score, Component Details Language Python 3 Frontend Streamlit, HTML/CSS NLP Frameworks spaCy, NLTK Embedding Model SentenceTransformers (all-MiniLM-L6-v2) Vector Database MongoDB (diseases vector collec- accuracy, and multiclass ROC AUC (one-vs-rest). Condence score calibration was done via Bayesian hyperparameter search over the regularization parameter C. 2. Quantitative Results Table 2: Classication Performance on Test Set Auth Database tion) SQLite Metric Value Speech Input OpenAI Whisper ASR Image Analysis CNN-based skin lesion classier 1. Backend Architecture The backend is built in Python 3 using object-oriented principles that maintain strict separation between NLP extraction, database interaction, and prediction logic. The VeterinaryNLPAnalyzer, MongoDiseaseRepository, LexicalPredictor, and SemanticVec- torEngine modules are independently created and combined within the main analysis pipeline. 2. Data Management A mixed database strategy is used. MongoDB Atlas stores un- structured disease JSON documents, multi-dimensional vector arrays, and analysis history records. SQLite manages structured user authentication and session data. Disease embeddings are pre-computed ofine and stored as native BSON arrays, allow- ing sub-second similarity retrieval during inference. 3. Frontend Interface The Streamlit web application provides a responsive, stateful interface supporting English and Malayalam language modes. Users can enter symptoms as free-form text, record voice input (processed via Whisper ASR), and optionally upload skin im- ages for dermatological analysis. Diagnostic results are shown as ranked disease cards with condence meters, urgency badges, and structured treatment recommendations. Analysis history is saved per user account across sessions. 4. Implementation Challenges Several practical challenges were faced during development. Variation in natural language symptom descriptions required extensive regex pattern libraries and negation-handling logic. Real-time semantic similarity computation was optimized by Macro-Average AUC 0.988 Micro-Average AUC 0.984 Weighted F1-Score (validation) 0.75 Accuracy (validation) 0.75 Best Regularization (C) 0.003594 Disease Database Coverage 205+ conditions Symptom Categories 30+ Figure 2: Multiclass ROC Curve for AVA Disease Classication (Macro-avg AUC = 0.988, Micro-avg AUC = 0.984) The ROC curve analysis (Figure 2) shows strong per-category discrimination. Category-specic AUCs include: species- specic (1.000), bacterial (1.000), respiratory (1.000), viral (0.963), and skin (0.964). The macro-average AUC of 0.988 conrms strong discriminative power across all disease cate- gories. The row-normalized confusion matrix (Figure 3) shows that the semantic vector engine achieves strongest performance on severe-category conditions (45% correctly classied as severe), with moderate conditions showing 43% correct classication. Mild conditions show distributed classication between moder- ate (50%) and severe (50%) bins, reecting the natural difculty of telling mild from moderate presentations in low-symptom descriptions. These results indicate that severity classication is the primary area for further improvement. Figure 3: Row-Normalized Confusion Matrix for Severity Classication 3. Comparison with Baseline Approaches Table 3: Comparison of Prediction Approaches Approach | Description | AUC ---|---|--- Keyword search | Simple term matching | Lexical Heuristic only | Rule-based scoring | 0.85 Semantic Vector only | Embedding similarity | 0.97 AVA Dual-Engine | Hybrid fusion | 0.988 Table 3 shows the advantage of the dual-engine hybrid approach over either component alone. The combination of heuristic pre- cision with semantic generalization consistently outperforms single-engine baselines, conrming the core architectural deci- sion. 9. COMPARISON STUDY Table 4 summarizes related works in veterinary and clinical AI, showing the gap that AVA addresses through its NLP-based, dual-engine, multimodal approach. 10. SOCIAL RELEVANCE AND SDGS AVA directly supports United Nations Sustainable Development Goal 3 (Good Health and Well-being) by encouraging timely animal health assessment and reducing the risk of condition worsening due to delayed treatment. The system makes prelimi- nary veterinary guidance more accessible, particularly in rural and underserved areas where qualied veterinarians may not be easily available. The system also adds to SDG 9 (Industry, Innovation and In- frastructure) by improving veterinary assessment through the integration of NLP and dense vector embeddings into a scalable, production-ready digital health infrastructure. The modular architecture supports extension to additional animal species, languages, and clinical domains. From a clinical workow perspective, AVA provides veterinari- ans with structured, pre-organized patient histories that reduce consultation time and cognitive load, improving overall care quality and efciency. 11. CONCLUSION This paper presented AVA, an AI-powered veterinary clinical decision support system that addresses the imporant need for accessible, accurate, and clear preliminary assessment tools in veterinary medicine. The system combines a Lexical Heuris- tic Engine and a Semantic Vector Engine in a dual-prediction design, achieving a macro-average AUC of 0.988 on a col- lected veterinary disease dataset. The modular pipeline, adaptive follow-up question generation, multi-language interface, and voice/image input capabilities together provide a comprehensive and production-ready solution. The study reviewed key design decisions and implementation challenges, showing important trade-offs between lexical preci- sion and semantic generalization. While disease classication performance is strong, severity grading remains the main area for further improvement, indicating the need for larger labeled veterinary datasets and ne-tuned domain-specic language models. Future research should focus on: (i) ne-tuning domain-specic veterinary language models on larger datasets; (ii) integrating telemedicine capabilities for real-time veterinarian consulta- tion; (iii) expanding species coverage to include avian, equine, and aquatic animals; (iv) applying zero-knowledge or federated learning approaches for privacy-preserving multi-clinic deploy- ment; and (v) conducting prospective clinical validation studies in real veterinary practice environments. ACKNOWLEDGMENT The authors express sincere gratitude to the Department of Com- puter Science and Engineering, Federal Institute of Science and Technology (FISAT), Angamaly, for providing the infrastructure and academic support necessary for this research. Special thanks to the faculty and peer reviewers whose constructive feedback strengthened the quality of this work. REFERENCES 1. Y.-G. Jin, G. Wu, J.-W. Seo, S.-J. Park, S.-H. Hur, D. Aliyeva, J.-H. Park, and K.-M. Kim, AI Veterinary As- sistance: Enhancing Clinical Decision-Making in Animal Healthcare, IEEE Access, 2025. 2. S. Agrawal et al., Large Language Models are Few-Shot Clinical Information Extractors, in Proc. EMNLP, 2022. Table 4: Comparison of Related Works Reference Methodology Advantage Limitation Agrawal et al. [2] Few-shot LLM clinical IE High precision extraction Low recall in sparse descriptions Mayats-Alpay [3] Image-based deep learning High diagnostic accuracy No text/voice input Ali et al. [4] 1D-CNN lung sound classication 90% accuracy, lightweight Single condition, single modality Jin et al. [1] Deep learning veterinary framework F1 = 0.88 No NLP or symptom text input Grout et al. [5] AI-based disease onset prediction 85% reliability Requires EHR data; not real-time AVA (Ours) NLP + Semantic Vector dual-engine AUC = 0.988, multilingual, voice/image Severity classication requires im- provement 3. L. Mayats-Alpay, Articial Intelligence for Automatic Detection and Classication of Disease on X-Ray Images, arXiv preprint, 2022. 4. A. S. W. Ali, M. M. Rashid, M. U. Yousuf, S. Shams et al., Towards Clinical Decision Support via Lung Sound Classication Using 1D-CNN, Sensors, 2024. 5. R. Grout, R. Gupta, R. Bryant, M. A. Elmahgoub et al., Predicting Disease Onset from Electronic Health Records for Population Health Management, Frontiers in Articial Intelligence, 2024. 6. OpenAI, GPT-4o Technical Overview and Large Lan- guage Model Applications, 2024. 7. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding, in Proc. NAACL-HLT, 2019. 8. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit et al., Attention Is All You Need, in Advances in Neural Infor- mation Processing Systems (NeurIPS), 2017. 9. S. Sharma et al., Deep Learning-Based Diagnosis and Prognosis of Alzheimers Disease: A Review, Medical AI Survey Study, 2022. 10. Kim et al., Deep Learning-Based Lung Cancer Diagnosis Using Respiratory Cytology Images, Clinical AI Study, 2023. ______________

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Real-time Network Device Configuration and Security Monitoring System Using NLP and LLM **DOI :****https://doi.org/10.5281/zenodo.19314735** Download Full-Text PDF Cite this Publication T. Suganya, M. Mohamed Apsal, L.V. Shriramsankar, B. Niranjan, 2026, Real-time Network Device Configuration and Security Monitoring System Using NLP and LLM, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 15, Issue 03 , March – 2026 * **Open Access** * Article Download / Views: 0 * **Authors :** T. Suganya, M. Mohamed Apsal, L.V. Shriramsankar, B. Niranjan * **Paper ID :** IJERTV15IS031259 * **Volume & Issue : ** Volume 15, Issue 03 , March – 2026 * **Published (First Online):** 29-03-2026 * **ISSN (Online) :** 2278-0181 * **Publisher Name :** IJERT * **License:** This work is licensed under a Creative Commons Attribution 4.0 International License __ PDF Version View __ Text Only Version #### Real-time Network Device Configuration and Security Monitoring System Using NLP and LLM T. Suganya(1), M. Mohamed Apsal(2), L.V. Shriramsankar(3), B. Niranjan(4) Assistant Professor(1), Students(234) Department of Computer Science and Engineering(Cybersecurity), K.L.N. College of Engineering, Pottapalayam, Sivagangai. AbstractModern enterprise networks contain a wide range of devices, services, and security challenges, making traditional manual configuration difficult and prone to human error. To address this issue, this work proposes a natural languagebased network automation and security monitoring system that simplifies device configuration and improves operational efficiency. In this system, network administrators can express high-level intents, such as enabling SSH access, configuring system logging, or checking device status, using simple natural language commands. These commands are processed using Natural Language Processing (NLP) techniques and Large Language Models (LLMs) to automatically generate the corresponding router configuration commands. The generated configurations are then applied within a simulated enterprise network environment for real-time device management. In addition to automation, the system continuously monitors network interfaces and device behavior to identify issues such as unauthorized port activity, interface failures, or unusual network events. When such conditions are detected, alerts are generated to notify administrators. By combining intent- based automation with real-time monitoring, the system reduces manual workload, decreases the likelihood of configuration errors, and improves overall network reliability and security. This solution demonstrates a practical and scalable approach for managing modern enterprise networks efficiently. Keywords: Network Automation, Intent-Based Networking, Natural Language Processing (NLP), Netmiko, Network Security Monitoring, Intrusion Detection System (IDS) 1. INTRODUCTION Todays organisation networks include a couple of routers, switches, and interconnected devices that require non-stop configuration and monitoring to ensure green operation and security. Historically, network directors configure these devices manually the use of command- line interfaces (CLI), which can be complicated, time- consuming, and vulnerable to human errors. As community length and complexity boom, guide configuration becomes inefficient and difficult to control, regularly leading to misconfigurations and protection vulnerabilities. Latest advancements in artificial intelligence and herbal Language Processing (NLP) have enabled the improvement of clever structures that simplify complex technical obligations. In networking, motive-based totally tactics allow administrators to define excessive- degree necessities in natural language, which can be routinely translated into device-unique configuration instructions. This reduces the dependency on guide CLI operations and improves ordinary community management efficiency. In this paper, an AI-based cause-pushed network automation and security tracking gadget is proposed. The system lets in users to enter network configuration intents in easy language, which are processed to generate corresponding router commands and deployed routinely the usage of SSH-based totally automation. in addition to configuration, the gadget constantly monitors community interfaces to stumble on unauthorized get right of entry to and interface screw ups. by way of combining automation with real-time tracking, the proposed system enhances network reliability, reduces administrative effort, and improves usual community security. 2. LITERATURE REVIEW / RELATED WORK Recent research has focused on improving network management through automation, intent-based networking, and intelligent systems. Several studies have explored different approaches to simplify configuration processes and enhance network security. INSpIRE: Integrated NFV-based intent refinement environment [1] proposed an intent-based framework that refines high-level user requirements into network configurations using Network Function Virtualization (NFV). The system focuses on translating user intent into actionable policies, improving flexibility in network management. However, it mainly emphasizes service orchestration rather than real-time monitoring. A comprehensive approach to the automatic refinement and verification of access control policies [2] introduced a method for automating the refinement and verification of access control policies. Their approach enhances network security by ensuring correctness in policy implementation. While effective in policy validation, it does not address dynamic configuration or real-time device-level monitoring. IBCS: Intent-based cloud services for security applications [3] presented an intent-based cloud service model designed for security applications. The system allows users to define security requirements at a higher level, which are then implemented automatically. Although it improves cloud security management, it is primarily focused on cloud environments rather than enterprise network devices. Hey, Lumi! Using natural language for intent based network management [4] explored the use of natural language interfaces for network management. Their work demonstrates how user inputs in plain language can be translated into network configurations. This approach improves usability, but it lacks integration with continuous monitoring and alert mechanisms. A survey on intent based networking [5] provided a comprehensive survey of intent-based networking technologies, highlighting their benefits, challenges, and future directions. The study emphasizes the importance of automation in modern networks but does not propose a complete implementation combining multiple functionalities. Intent-driven autonomous network and service management in future cellular networks: A structured literature review [6] reviewed intent-driven network management approaches in next-generation cellular networks. The authors discussed the role of automation and intelligence in managing complex systems, but their focus is mainly on large-scale telecom infrastructures. From the analysis of existing works, it is observed that most solutions focus on either intent-based automation or security aspects independently. Very few systems integrate natural language-based configuration, automated deployment, and real-time monitoring into a single framework. To address this gap, the proposed system combines NLP-based intent processing with automated configuration and continuous network monitoring, providing a more comprehensive and practical solution for modern enterprise networks. 3. PROPOSED SYSTEM The proposed system is an intelligent network automation and security monitoring solution that integrates Natural Language Processing (NLP) with automated configuration and real-time monitoring. The primary objective of the system is to simplify network management by allowing administrators to interact with network devices using high-level natural language commands instead of manual command-line configuration. In this system, the user provides input in the form of simple text instructions through a web-based interface. These instructions may include tasks such as enabling SSH access, configuring IP addresses, setting up routing protocols, or checking device status. The system processes the input using an intent analysis mechanism to identify the required network operation. Once he intent is identified, the command generation module converts the users request into device-specific configuration commands. These commands are structured according to the syntax supported by network devices. The generated commands are then securely deployed to the target device using an SSH-based automation module, ensuring safe and reliable communication. In addition to configuration automation, the system includes a continuous monitoring component that observes the status of network interfaces in real time. The monitoring module checks for abnormal conditions such as unauthorized interfaces becoming active, trusted interfaces going down, or unusual device behavior. When such anomalies are detected, the system triggers an alert mechanism that notifies the administrator through email. This ensures that network issues are identified and addressed at an early stage, reducing the risk of failures and security threats. The integration of natural language-based automation with real-time monitoring makes the proposed system efficient, user-friendly, and reliable. It significantly reduces manual effort, minimizes configuration errors, and enhances overall network security. Compared to existing systems, the proposed solution provides a unified approach by combining configuration, monitoring, and alerting within a single framework. processing. The intent processing module analyzes the input and identifies the required network operation using predefined keywords or rules. Based on the identified intent, the command generation module creates device-specific configuration commands. These commands are then executed on the target device through the deployment module, which establishes a secure SSH connection using automation tools such as Netmiko. After configuration, the monitoring module continuously checks the status of network interfaces and device activity. The alert module detects abnormal conditions such as unauthorized access or interface failures and notifies the administrator through email. This architecture enables automated configuration and real-time monitoring in a single system, improving efficiency, reducing errors, and enhancing network security. Fig 1. Data flow diagram of proposed system 4. SYSTEM ARCHITECTURE The proposed system follows a modular architecture that integrates user interaction, automation, and monitoring to manage network devices efficiently. Each module performs a specific function and collectively provides a complete network management solution. The process begins with a web-based user interface, where the administrator provides instructions in natural language. These inputs are sent to the backend server for Fig 2. System architecture diagram 5. IMPLEMENTATION & METHODOLOGY 1. System Design Overview The system is designed as a modular architecture that integrates user interaction, intent processing, command generation, configuration deployment, and network monitoring. Each module works independently but is connected to form a complete automated network management system. 2. Development Environment The implementation is carried out using Python as the primary programming language due to its simplicity and strong support for network automation. A web interface is developed using the Flask framework to allow user interaction. The network environment is simulated using a router setup, enabling safe testing of configurations and monitoring features. 3. Intent Processing Mechanism The system accepts user input in the form of natural language. The intent processing module analyzes the input using keyword-based logic to identify the required network operation. Based on the detected keywords such as SSH, gateway, or OSPF, the system determines the appropriate configuration task. 4. Command Generation Process Once the intent is identified, the command generation module converts the user request into device-specific configuration commands. These commands are structured in the format supported by network devices, ensuring compatibility and correct execution. 5. Configuration Deployment The generated commands are deployed to the network device using a secure SSH connection. The deployment module establishes communication with the router and sends the commands automatically. This eliminates the need for manual configuration and ensures consistent execution of network operations. 6. Network Monitoring Mechanism After configuration, the system continuously monitors the network device by executing standard diagnostic commands. It retrieves interface status information and analyzes it to identify abnormal conditions such as inactive interfaces or unexpected activity. 7. Intrusion Detection Logic The monitoring module uses a rule-based approach to detect anomalies. It compares active interfaces with a predefined list of authorized interfaces. If an unauthorized interface becomes active or a critical interface goes down, the system identifies it as a potential issue and generates an alert. 8. Alert Generation and Notification When an abnormal condition is detected, the system generates an alert message and notifies the administrator. The alert mechanism includes email notification using secure communication protocols, ensuring that the administrator is informed in real time. 9. User Interface Interaction The web-based interface allows users to enter network intents and view system responses. After submitting an input, the user receives feedback such as generated commands, deployment status, and monitoring results, providing a simple and interactive experience. 10. Periodic Monitoring In addition to manual checks, the system supports periodic monitoring at fixed time intervals. This ensures continuous observation of the network and helps in early detection of issues without requiring user intervention. Fig 3. The gateway is configured via our system by giving prompt VIII. CONCLUSION The proposed system provides an effective solution for simplifying network management by integrating intent- based automation with continuous monitoring. It enables administrators to give high-level instructions in natural language, which are automatically converted into device- specific configuration commands and deployed efficiently. This approach reduces manual effort, minimizes human errors, and improves the overall efficiency of network configuration. Fig 4. Email alert occured when the trusted and untrusted interface is down and up respectively 6. EXISTING SYSTEM VS PROPOSED SYSTEM Table 1. Aspects of existing system and proposed system 7. PERFORMANCE EVALUATION Fig 5. performance evaluation of existing system and proposed system In addition to automation, the system continuously monitors network interfaces to detect issues such as unauthorized activity and device failures. The alert mechanism ensures timely notification, allowing quick response to potential problems. By combining automation, monitoring, and alerting in a single framework, the system enhances network reliability, security, and ease of management in modern network environments. IX. REFERENCES 1. E. J. Scheid et al., INSpIRE: Integrated NFV-based intent refinement environment, in Proc. IFIP/IEEE Symp. Integr. Netw. Service Manag., 2017. 2. M. Cheminod, L. Durante, L. Seno, F. Valenza, and 1. Valenzano, A comprehensive approach to the automatic refinement and verification f access control policies, Comput. Security, vol. 80, pp. 186199, Jan. 2019. 3. J. Kim et al., IBCS: Intent-based cloud services for security applications, IEEE Commun. Mag., vol. 58, no. 4, pp. 4551, Apr. 2020. 4. A. S. Jacobs et al., Hey, Lumi! Using natural language for intent based network management, in Proc. USENIX ATC, Jul. 2021. 5. A. Leivadeas and M. Falkner, A survey on intent based networking, IEEE Commun. Surveys Tuts., vol. 25, no. 1, pp. 625655, 1st Quart., 2023. 6. K. Mehmood, K. Kralevska, and D. Palma, Intent- driven autonomous network and service management in future cellular networks: A structured literature review, Comput. Netw., vol. 220, Jan. 2023. ______________

Real-time Network Device Configuration and Security Monitoring System Using NLP and LLM View Abstract & download full text of Real-time Network Device Configuration and Security Monitoring Syst...

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