Key Takeaways from the Take Command Summit 2025: Inside the SOC – Expert Stories from the Frontlines of Threat Hunting and Malware Detection

What does it really look like to detect, contain, and respond to modern cyber threats in real time? At the Take Command 2025 Virtual Cybersecurity Summit, Inside the SOC session offered a behind-the-scenes look at how security teams are tackling everything from ransomware staging to advanced soci...

ML-Enhanced AES Anomaly Detection for Real-Time Embedded Security

Advanced Encryption Standard AES is a widely adopted cryptographic algorithm, yet its practical implementations remain susceptible to side-channel and fault injection attacks. In this work, we propose a comprehensive framework that enhances AES-128 encryption security through controlled anomaly...

Intelligent ARP Spoofing Detection Using Multi-Layered Machine Learning (ML) Techniques for IoT Networks

Address Resolution Protocol ARP spoofing remains a critical threat to IoT networks, enabling attackers to intercept, modify, or disrupt data transmission by exploiting ARP's lack of authentication. The decentralized and resource-constrained nature of IoT environments amplifies this vulnerability,...

ZKTeco ZKBio Time Detection

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MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem

Malicious package detection has become a critical task in ensuring the security and stability of the PyPI. Existing detection approaches have focused on advancing model selection, evolving from traditional machine learning ML models to large language models LLMs. However, as the complexity of the...

Network Threat Detection: Addressing Class Imbalanced Data with Deep Forest

With the rapid expansion of Internet of Things IoT networks, detecting malicious traffic in real-time has become a critical cybersecurity challenge. This research addresses the detection challenges by presenting a comprehensive empirical analysis of machine learning techniques for malware detecti...

SDN-Based False Data Detection with Its Mitigation and Machine Learning Robustness for In-Vehicle Networks

As the development of autonomous and connected vehicles advances, the complexity of modern vehicles increases, with numerous Electronic Control Units ECUs integrated into the system. In an in-vehicle network, these ECUs communicate with one another using an standard protocol called Controller Are...

Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for...

Streamlining HTTP Flooding Attack Detection through Incremental Feature Selection

Applications over the Web primarily rely on the HTTP protocol to transmit web pages to and from systems. There are a variety of application layer protocols, but among all, HTTP is the most targeted because of its versatility and ease of integration with online services. The attackers leverage the...

ChestyBot: Detecting and Disrupting Chinese Communist Party Influence Stratagems

Foreign information operations conducted by Russian and Chinese actors exploit the United States' permissive information environment. These campaigns threaten democratic institutions and the broader Westphalian model. Yet, existing detection and mitigation strategies often fail to identify active...

AttentionGuard: Transformer-Based Misbehavior Detection for Secure Vehicular Platoons

Vehicle platooning, with vehicles traveling in close formation coordinated through Vehicle-to-Everything V2X communications, offers significant benefits in fuel efficiency and road utilization. However, it is vulnerable to sophisticated falsification attacks by authenticated insiders that can...

GPML: Graph Processing for Machine Learning

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML Graph Processing for Machine Learning library addresses this need by transforming raw network traffic traces into graph representations, enabling...

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

The increasing complexity and scale of the Internet of Things IoT have made security a critical concern. This paper presents a novel Large Language Model LLM-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on...

Mitigating Backdoor Triggered and Targeted Data Poisoning Attacks in Voice Authentication Systems

Voice authentication systems remain susceptible to two major threats: backdoor triggered attacks and targeted data poisoning attacks. This dual vulnerability is critical because conventional solutions typically address each threat type separately, leaving systems exposed to adversaries who can...

Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks

As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major...

Native Sensors vs. Integrations for XDR Platforms?

Native sensors vs. integrations in XDR: Native sensors offer faster deployment, real-time detection, and deeper visibility, while integrations may add complexity and delays. Learn how to optimize your XDR strategy for improved security...

Harnessing AI for Proactive Threat Intelligence and Advanced Cyber Defense

Discover how AI revolutionizes cybersecurity with real-time threat detection, adaptive protection, and advanced data protection to combat evolving…...

Sailing Securely Across the SDLC: Introducing Wiz's Image Trust and Kubernetes Audit Log Collector

Secure your applications across the SDLC by deploying only trusted images and monitoring your Kubernetes control plane in near-real time to detect potential threats...

Microsoft Defender for Endpoint now stops human-operated attacks on its own

Defenders need every edge they can get in the fight against ransomware. Today, were pleased to announce that Microsoft Defender for Endpoint customers will now be able automatically to disrupt human-operated attacks like ransomware early in the kill chain without needing to deploy any other...

Activities in the Cybercrime Underground Require a New Approach to Cybersecurity

As Threat Actors Continuously Adapt their TTPs in Today's Threat Landscape, So Must You Earlier this year, threat researchers at Cybersixgill released the annual report, The State of the Cybercrime Underground. The research stems from an analysis of Cybersixgill's collected intelligence items...