Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework

The increasing penetrations of the critical infrastructure sector in the United States with intelligent digital technologies have greatly increased exposure to advanced cyber adversaries and operational vulnerabilities. AI-powered governance and automated decision-making systems are becoming a ke...

Context-Aware Web Attack Detection in Open-Source SIEM Systems Via MITRE ATT&CK-Enriched Behavioral Profiling

Security Information and Event Management SIEM systems aggregate log data from heterogeneous sources to detect coordinated attacks. Traditional rule-based correlation engines struggle to classify multi-step web application attacks because they examine each event without reference to the behaviour...

Explainability Methods for Hardware Trojan Detection: A Systematic Comparison

Hardware trojan detection requires accurate identification and interpretable explanations for security engineers to validate and act on results. This work compares three explainability categories for gate-level trojan detection on the Trust-Hub benchmark: 1 domain-aware property-based analysis of...