Original post on biometricupdate.com

Reality Defender strikes deal to provide deepfake detection to French Orange AI security firm Reality Defender will provide its deepfake detection capabilities to Orange Business, the enterprise di...

#Biometrics #News #Trade #Notes #Voice #Biometrics […]

[Original post on biometricupdate.com]

Original post on biometricupdate.com

Reality Defender strikes deal to provide deepfake detection to French Orange AI security firm Reality Defender will provide its deepfake detection capabilities to Orange Business, the enterprise di...

#Biometrics #News #Trade #Notes #Voice #Biometrics […]

[Original post on biometricupdate.com]

Abstract: There is a need for in-field actinide measurements in support of nuclear forensic, safeguards, and environmental monitoring missions. Traditional methods of inorganic/elemental analysis, such as inductively coupled plasma mass spectrometry (ICP-MS), have high operational overheads, making these platforms ill-suited for this task. The liquid sampling–atmospheric pressure glow discharge (LS-APGD) ionization source is a proven microplasma ionization source with significantly reduced operational overhead as compared to ICP-MS; however, most studies to date have focused on coupling the LS-APGD to an ultrahigh resolution Orbitrap mass spectrometer. While the Orbitrap mass spectrometer is a benchtop instrument, it is quite complex with a large footprint and requires extremely low mass analyzer pressures. The Advion ExpressionL compact mass spectrometer (CMS) is a compact, easily transported single quadrupole mass spectrometer platform that was previously coupled with the LS-APGD to measure multielement/metal solutions, albeit not actinides. To this end, this manuscript reports the optimization of the LS-APGD with the Advion ExpressionL CMS mass spectrometer platform specifically for in-field actinide (uranium and thorium) measurements. This is the first report on the optimization of the dual-electrode LS-APGD on the CMS, including a modified ion sampling geometry. This also includes the first analysis of thorium using the LS-APGD, regardless of mass spectrometer coupling. After establishing that the LS-APGD and the mass spectrometer operations could be optimized independently, the LS-APGD discharge conditions were optimized with a design of experiments approach, with the mass spectrometer parameters optimized by a full factorial study. Once fully optimized, limits of detection of 0.2 ng total analyte mass were found for both uranium and thorium, below the EPA requirements for drinking water.

New from Applied Spectroscopy!
Development of a Field-Deployable #Mass #Spectrometry System for Nuclear Forensics Applications Using Liquid Sampling–Atmospheric Pressure Glow Discharge as an Ion Source
https://doi.org/10.1177/00037028251405294
#SAS #Spectroscopy #thorium #uranium #detection

The Axios npm supply chain incident: fake dependency, real backdoor On March 31, 2026, two malicious Axios versions (1.14.1 and 0.30.4) were briefly published to npm via a compromised maintainer ac...

#Blue #Team #Detection #Engineering #Threat #Hunting […]

[Original post on blog.nviso.eu]

Original post on biometricupdate.com

Cybersecurity pro demonstrates IAD importance for facial recognition at RSAC 2026 Jake Moore believes the identity stack is broken. But instead of just willing his belief into reality, the global c...

#Biometrics #News #Facial #Recognition #Liveness […]

[Original post on biometricupdate.com]

Original post on biometricupdate.com

Cybersecurity pro demonstrates IAD importance for facial recognition at RSAC 2026 Jake Moore believes the identity stack is broken. But instead of just willing his belief into reality, the global c...

#Biometrics #News #Facial #Recognition #Liveness […]

[Original post on biometricupdate.com]

Threat Detection Software Threat detection software has become an essential pillar of modern cybersecurity as organizations face a rapidly evolving threat landscape driven by automation, artificial intelligence, and increasingly sophisticated attack techniques. In today’s hyperconnected digital environment, businesses rely heavily on cloud platforms, remote work infrastructure, SaaS applications, APIs, and interconnected systems that significantly expand the attack surface. As a result, traditional security tools that depend on static rules or signature-based detection are no longer sufficient. Organizations now require intelligent, adaptive, and real-time threat detection capabilities that can identify both known and unknown threats before they cause damage. ## **What Is Threat Detection Software?** Threat detection software is a cybersecurity solution designed to monitor, analyze, and identify suspicious or malicious activities across an organization’s IT environment. It works by collecting data from networks, endpoints, cloud systems, identities, and applications, then applying advanced analytics to detect anomalies and potential threats. Unlike traditional tools that rely only on known threat signatures, modern threat detection software uses artificial intelligence, machine learning, and behavioral analytics to identify unusual patterns and emerging threats in real time. This enables organizations to detect cyberattacks at an early stage, even if they have never been seen before. ## **Why Is Threat Detection Software Important?** Threat detection software is critical because cyber threats are no longer simple or predictable. Attackers use advanced techniques such as fileless malware, credential theft, ransomware, and lateral movement to bypass traditional defenses. Without modern detection capabilities, these threats can remain hidden for weeks or even months. The importance of threat detection software lies in its ability to: * Detect threats in real time before they cause damage * Reduce the risk of data breaches and financial loss * Provide full visibility across cloud, network, endpoint, and identity layers * Minimize alert fatigue through intelligent prioritization * Enable faster and automated incident response * Support compliance and regulatory requirements Organizations that implement advanced threat detection software can shift from reactive security to proactive defense, significantly improving their overall security posture. ## **How Threat Detection Software Works** Threat detection software operates by continuously collecting and analyzing large volumes of data from across the organization. This includes system logs, network traffic, user activity, API interactions, and cloud events. The system establishes a baseline of normal behavior and then identifies deviations that may indicate malicious activity. For example, if a user suddenly logs in from an unusual location, accesses sensitive data at odd hours, or triggers abnormal system activity, the software flags this behavior as suspicious. It then correlates this information with other signals to determine whether it represents a real threat. Advanced platforms use AI-driven correlation to connect multiple low-risk signals into a high-confidence threat, providing a complete view of the attack lifecycle rather than isolated alerts. ## **Key Features of Modern Threat Detection Software** Modern threat detection software includes several advanced capabilities that make it more effective than traditional security tools. Real-time monitoring ensures continuous visibility across the entire environment, enabling immediate detection of suspicious activity. Behavioral analytics allow the system to understand normal patterns and identify anomalies. AI and machine learning enhance detection accuracy and reduce false positives. Automated response capabilities enable immediate action to contain threats. Unified visibility consolidates data from multiple sources into a single platform, improving efficiency and context. Platforms such as those offered by **Seceon** integrate these capabilities into a unified, AI-driven security platform that delivers faster detection, reduced alert fatigue, and automated response across complex environments. ## **Types of Threats Detected by Threat Detection Software** Threat detection software is designed to identify a wide range of cyber threats across modern IT environments, including cloud, network, endpoint, and identity layers. By leveraging AI, machine learning, and behavioral analytics, it can detect both known and unknown threats in real time. Below are the key types of threats detected by advanced threat detection software. ### **1. Malware Attacks** Threat detection software identifies malicious software such as viruses, worms, trojans, and spyware by analyzing system behavior, file activity, and execution patterns. Modern solutions can detect fileless malware that operates in memory without leaving traditional signatures. ### **2. Ransomware Attacks** Ransomware is one of the most damaging cyber threats. Threat detection platforms monitor unusual encryption activity, rapid file changes, and suspicious processes to detect and stop ransomware before it spreads across systems. ### **3. Phishing and Social Engineering Attacks** Advanced threat detection software can identify phishing attempts by analyzing email patterns, domain reputation, and user behavior. It helps prevent credential theft and unauthorized access resulting from social engineering attacks. ### **4. Insider Threats** Not all threats come from outside the organization. Insider threats—whether malicious or accidental—can be detected through user behavior analytics that identify unusual access patterns, data transfers, or privilege misuse. ### **5. Credential Theft and Identity-Based Attacks** Attackers often target user credentials to gain access to systems. Threat detection software monitors login behavior, detects anomalies such as impossible travel or abnormal access times, and identifies suspicious authentication activity. ### **6. Advanced Persistent Threats (APTs)** APTs are long-term, targeted attacks that remain undetected while extracting sensitive data. Threat detection software identifies these threats by correlating multiple low-level signals over time to uncover hidden attack patterns. ### **7. Network-Based Attacks** These include distributed denial-of-service (DDoS) attacks, lateral movement, and command-and-control (C2) communication. By analyzing network traffic and flow data, threat detection software detects abnormal communication patterns and suspicious connections. ### **8. Data Exfiltration Attempts** Threat detection solutions monitor unusual data transfers, large outbound traffic, or access to sensitive files to detect potential data exfiltration attempts before critical information is lost. ### **9. Zero-Day Threats** Zero-day attacks exploit unknown vulnerabilities. AI-driven threat detection software identifies these threats by detecting abnormal behavior rather than relying on known signatures. ### **10. Cloud Security Threats** In cloud environments, threats such as misconfigurations, unauthorized access, and suspicious API activity are common. Threat detection software provides real-time monitoring and visibility across multi-cloud and hybrid infrastructures. ### **11. Endpoint Attacks** Endpoints are frequent entry points for attackers. Threat detection software monitors device behavior, processes, and system changes to detect malware, unauthorized access, and suspicious activities. ### **12. Botnet Activity** Botnets consist of compromised devices controlled remotely by attackers. Threat detection platforms identify botnet communication patterns and prevent devices from participating in malicious networks. ## **The Role of AI and Automation in Threat Detection** Artificial intelligence and automation are transforming how threat detection software operates. AI enables systems to analyze massive datasets, identify patterns, and detect anomalies that would be impossible for humans to identify manually. Machine learning models continuously improve detection accuracy by learning from new data. Automation plays a crucial role in response. When a threat is detected, automated workflows can isolate affected systems, block malicious activity, or enforce additional security controls. This reduces response time from hours to seconds and minimizes the impact of cyber incidents. ## **Benefits of Threat Detection Software for Businesses** Threat detection software provides several key benefits that directly impact business performance and resilience. It improves security by identifying threats early and preventing breaches. It enhances operational efficiency by reducing manual workload and alert fatigue. It supports compliance by providing detailed logs and reports. It enables scalability by adapting to growing environments and increasing data volumes. Most importantly, it builds trust by protecting sensitive data and ensuring the continuity of business operations. ## **Frequently Asked Questions (FAQs) About Threat Detection Software** ### **1. What is threat detection software used for?** Threat detection software is used to monitor and analyze systems, networks, and user activity to identify potential cyber threats in real time and prevent security breaches. ### **2. How does threat detection software detect unknown threats?** It uses AI, machine learning, and behavioral analytics to identify anomalies and unusual patterns that may indicate new or unknown threats, even without predefined signatures. ### **3. What is the difference between threat detection and threat prevention?** Threat detection focuses on identifying potential threats, while threat prevention aims to block or stop those threats before they cause harm. Modern solutions often combine both capabilities. ### **4. Can threat detection software reduce false positives?** Yes, advanced platforms use AI-driven correlation and behavioral analysis to significantly reduce false positives and improve alert accuracy. ### **5. Is threat detection software suitable for cloud environments?** Yes, modern threat detection solutions are designed to monitor cloud environments, detect misconfigurations, and identify suspicious activity across multi-cloud and hybrid infrastructures. ### **6. How does threat detection software help with compliance?** It provides continuous monitoring, detailed logging, and automated reporting, helping organizations meet regulatory requirements such as GDPR, HIPAA, and PCI-DSS. ### **7. What industries need threat detection software the most?** All industries benefit from threat detection software, especially finance, healthcare, government, manufacturing, and any organization handling sensitive data. ## **Conclusion: Building a Proactive Cybersecurity Strategy** Threat detection software is no longer optional in today’s digital landscape. As cyber threats continue to evolve, organizations must adopt intelligent, AI-driven solutions that provide real-time visibility, advanced analytics, and automated response. By integrating threat detection into a unified cybersecurity strategy, businesses can detect threats earlier, respond faster, and reduce the overall impact of cyber incidents. Investing in modern threat detection software enables organizations to move beyond reactive defense and build a proactive, resilient security posture that supports long-term growth and innovation. The post Threat Detection Software appeared first on Seceon Inc. *** This is a Security Bloggers Network syndicated blog from Seceon Inc authored by Pushpendra Mishra. Read the original post at: https://seceon.com/threat-detection-software/

Threat Detection Software Threat detection software has become an essential pillar of modern cybersecurity as organizations face a rapidly evolving threat landscape driven by automation, artificial...

#Security #Bloggers #Network #aiMSSP #aiSIEM #aiXDR #threat #detection

Origin | Interest | Match

White text on a dark blue background reads, “Early-stage deepfake detection market on rise: UK report”. Underneath, CoinGeek is listed as the source.

“Early-stage deepfake detection market on rise: UK report” — CoinGeek

#DeepfakeNews #Detection #Deepfake

White text on a dark blue background reads, “Orange Business Integrates AI and Deepfake Detection”. Underneath, Security Today is listed as the source.

“Orange Business Integrates AI and Deepfake Detection” — Security Today

#DeepfakeNews #Detection #AI

Sprucing up the look to one of my favourite works.. #MentalDentalMurderByProxy #MDMBP more in keeping with the style and content of the book... If you want #MedicalHorror #excitement #detection and #inovation... Just check out this beaut
Available to read on Amazon Books

White text on a dark blue background reads, “Resemble AI unveils deepfake detection tools amid synthetic media surge”. Underneath, SC Media is listed as the source.

“Resemble AI unveils deepfake detection tools amid synthetic media surge” — SC Media

#DeepfakeNews #Detection #AI

Abstract: Chromium-doped zinc selenide (Cr:ZnSe) crystals are the gain media of choice for mid-infrared lasers operating over a 1.9–3.4 µm spectral range. In this study, we used laser-induced breakdown spectroscopy (LIBS) of Cr:ZnSe polycrystalline materials to improve the sensitivity of detecting chromium concentration in the laser-active materials. The fundamental harmonic of a Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG) laser was used as an excitation source. After calibration of the LIBS signal, we calculated that chromium's limit of detection (LOD) was 30 parts per million (ppm). Normalization of the Cr(I) intensity peak at 357.9 nm by the square root of the Zn(I) peak at 636.2 nm reduced the LOD to 20 ppm and increased the coefficient of determination to R² ≈ 0.98. These results demonstrate the potential of LIBS for microscale mapping of dopant distributions in laser crystals and for on-site monitoring of material quality during fabrication.

New from Applied Spectroscopy!
Application of Laser-Induced Breakdown Spectroscopy for Measuring Chromium Concentration in Chromium-Doped Zinc Selenide (Cr:ZnSe) Laser Materials
Read more: https://doi.org/10.1177/00037028251401252
#SAS #Spectroscopy #LIBS #Zinc #selenide #chromium #detection

Awakari App

When a single pane of glass becomes a one-way mirror [Q&A] Consolidating cybersecurity information into a ‘single pane’ has been something of a holy grail for the industry in recent years. ...

#Article #cybersecurity #DevOps #Q&A #threat #detection

Origin | Interest | Match

Original post on helpnetsecurity.com

Malware detectors trained on one dataset often stumble on another Machine learning models built to catch malware on Windows systems are typically evaluated on data that closely resembles their trai...

#News #cybersecurity #ENISA #machine #learning […]

[Original post on helpnetsecurity.com]

The AIX Blind Spot – Getting Visibility Where EDR Can’t Run The post The AIX Blind Spot – Getting Visibility Where EDR Can’t Run appeared first on Nextron Systems . AIX is still running cri...

#THOR #AIX #antivirus #apt #EDR #IBM #scan #scanner #Threat #Detection

Origin | Interest | Match

The AIX Blind Spot – Getting Visibility Where EDR Can’t Run The post The AIX Blind Spot – Getting Visibility Where EDR Can’t Run appeared first on Nextron Systems . AIX is still running cri...

#THOR #AIX #antivirus #apt #EDR #IBM #scan #scanner #Threat #Detection

Origin | Interest | Match

MultiSearch Tag Explorer MultiSearch Tag Explorer - Explore tags and search results by aéPiot - aéPiot: Independent SEMANTIC Web 4.0 Infrastructure (Est. 2009). High-density Functional Semantic Connectivity with 100/100 Trust...

#AIRPORT #SURFACE #DETECTION #EQUIPMENT
multi-search-tag-explorer.aepiot.com/advanced-sea...
aepiot.ro

The title of my substack piece: “AI Detection, Part 2: Can a thief recognise a thief?” above the logo of a company called ‘Undercover AI’.

5. Thus, Can AI detect if something is written by AI? Here’s my experience:

jetse.substack.com/p/ai-detecti...

#AI #slop #detection #writing #publishing

4. Of cours, authors can test their AI-revised text in AI detection tools, and then revise it again (some tools offer that as a—paid—service). It seems more work than outright writing it yourself, then again, some people are crazy.

#AI #slop #detection #writing #publishing

‘Soon publishers won’t stand a chance’: literary world in struggle to detect AI-written books US release of horror novel Shy Girl cancelled and UK book discontinued after suspected AI use, as publishers feel ‘cold shiver’

2. And just before I’m ready to launch part 2 of my AI detextion essay, the Guardian checks in:

www.theguardian.com/technology/2...

#AI #slop #detection #writing #publishing

AI Detection, Part 1 Can an AI detect the real thing?

1. ICYMI, yesterday I posted this:

jetse.substack.com/p/ai-detecto...

#AI #slop #detection #writing #publishing

From Logs to Context: Why Your SOC Detection Needs a Real-Time Context and Control Layer In the traditional pipeline, every step after ingestion is retroactive. The SIEM owns the data, and all analysis depends on querying stored records. Alternatively, the new architecture inserts a real-...

The reality of most #SOC pipelines: #Detection happens after ingestion & storage. By the time you query logs in your #SIEM, the attack is already underway.

💡 In our CEO Ting Wang's latest blog, we explore why you need a #realtime context & control layer.

www.timeplus.com/post/from-lo...

White text on a dark blue background reads, “UK deepfake detection report forecasts continued growth for sector”. Underneath, Biometric Update is listed as the source.

“UK deepfake detection report forecasts continued growth for sector” — Biometric Update

#DeepfakeNews #Detection #Deepfakes

White text on a dark blue background reads, “Securonix Highlights Targeted Phishing Research to Underscore Advanced Threat Detection Focus”. Underneath, TipRanks is listed as the source.

“Securonix Highlights Targeted Phishing Research to Underscore Advanced Threat Detection Focus” — TipRanks

#PhishingNews #Phishing #Detection

Timeplus Webinar | Build Real-Time Security Context & Control to Detect Threats Before SIEM In our March webinar hosted by our Co-Founders Ting Wang (CEO) and Gang Tao (CTO), we’ll break down how a real-time security context and control layer actually works, and why it’s the missing piece in...

AI era attacks happen at machine scale & speed. #Detection fails not because of logic, but because context arrives too late.

Our Co-Founders Ting Wang & @gangtao.bsky.social share how a #realtime security context & control layer is the missing piece.

🎟️ Webinar: www.timeplus.com/webinar-logs...

Original post on biometricupdate.com

​Jumio approved for biometric KYC in Malaysia with successful independent assessment Jumio has completed the Malaysian central bank’s (Bank Negara Malaysia) independent eKYC Breakthrough Assess...

#Biometrics #News #Financial #Services #Liveness […]

[Original post on biometricupdate.com]

Poirot spells out the danger in this Jack Faulks illustration for 'Triangle At Rhodes' in the May 1936 edition of 'The Strand Magazine'.
#AgathaChristie #Poirot #detection #vintagecrime
@flackjd.bsky.social @greatdetective.bsky.social @collectchristie.bsky.social