Adversarial RL for Port-Scan Evasion: Attacker Feature Visibility in Edge-Deployed IDS

Machine learning-based intrusion detection systems IDS are increasingly used in resource-constrained Internet of Things IoT environments, yet their robustness is often evaluated against static attacks rather than adversaries that adapt to detection feedback. This paper investigates adaptive...

Bounded Reasoning: Cognitive Hierarchy in Human-Versus-AI Cyber Defense

Human-agent evaluations often compress interaction into a single performance score, even when human and automated policies adapt differently over time. We study this in a sequential cyber-defense game on an attack graph, where a human or reinforcement-learning defender protects cloud assets again...

Dueling Deep Q-Learning for Intrusion Detection

Intrusion detection systems IDS and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a...