SENTINEL-RL: Offloading Topological Reasoning from LLM Agents in the Security Operations Center

Large language model LLM agents are increasingly proposed as autonomous SOC analysts, but two limitations make them unreliable at enterprise scale: a finite context window cannot hold a multi-thousand-host authentication graph, and free-form generation offers no guarantee that a recommended...

Graph Neural Networks at War: Integrating Cybersecurity and Drone Intelligence in the Israeli-Iranian Conflict

Physical cyber systems have brought about new threats and challenges in detection and immediate response. This study examines how Graph Neural Networks GNNs can be used to aid cybersecurity and drone management in a physical cyber system comprising of cyber intrusions and unmanned aerial vehicles...

Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response

Autonomous network-security response systems promise to reduce Security Operations Centre SOC reaction latency, but reward-only multi-agent reinforcement learning MARL can improve security reward while remaining non-deployable. We present a safety-contract graph MARL framework and instantiate it ...

Towards Predicting Multi-Vulnerability Attack Chains in Software Supply Chains from Software Bill of Materials Graphs

Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials SBOM-based pipelines for security analysis typically treat scanner findings as independent per-CVE Common...

How the Graph Construction Technique Shapes Performance in IoT Botnet Detection

The increasing incidence of IoT-based botnet attacks has driven interest in advanced learning models for detection. Recent efforts have focused on leveraging attention mechanisms to model long-range feature dependencies and Graph Neural Networks GNNs to capture relationships between data instance...

Cascaded Vulnerability Attacks in Software Supply Chains

Most of the current software security analysis tools assess vulnerabilities in isolation. However, sophisticated software supply chain security threats often stem from cascaded vulnerability and security weakness chains that span dependent components. Moreover, although the adoption of Software...

A Graph-Attentive LSTM Model for Malicious URL Detection

Malicious URLs pose significant security risks as they facilitate phishing attacks, distribute malware, and empower attackers to deface websites. Blacklist detection methods fail to identify new or obfuscated URLs because they depend on pre-existing patterns. This work presents a hybrid deep...

Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks

Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability...