The paper proposes a Cognitive Threat Intelligence and Explainable Federated Security Analytics framework to enable privacy-preserving and scalable cyber threat detection across distributed infrastruc…

This paper develops and analyzes various ensemble models, culminating in an XGBoost-based system, to reliably detect UAV intrusions using XAI and advanced statistical methods to pinpoint the root caus…

This paper proposes a hybrid CNN-LSTM framework to enhance cyber attack detection and prevention in U.S. critical digital infrastructure by evaluating multiple machine learning models on the CSE-CIC-I…

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

The paper empirically characterizes 'shadow AI'—the unsanctioned use of frontier AI in critical infrastructure—as a systemic threat that erodes established assurance and security controls.

This paper evaluates and compares HAZOP and Bow-Tie analysis, demonstrating that while both are useful for cyber risk assessment in hydropower, a coordinated adversary can bypass conventional safeguar…

LanG is a governance-aware, open-source agentic AI platform that unifies security operations by providing advanced correlation, automated rule generation, and attack reconstruction capabilities.

This paper proposes an explainable threat attribution system for IoT networks that uses SHAP and flow behavior modeling to accurately classify and explain over 30 distinct attack variants into 8 meani…

The paper proposes XAI FL-IDS, a novel framework that combines Federated Learning and SHAP-based explainability to build a privacy-preserving and highly accurate distributed Intrusion Detection System…

The paper proposes a management framework, using a governed AI query-broker artifact, to safely integrate generative AI into high-risk operational decision support, such as Security Operations Centers…

The paper proposes a comprehensive, dual-layer architectural framework for AI identification and traceability, ensuring continuous accountability and regulatory oversight throughout the entire lifecyc…

The paper proposes an ensemble learning framework combined with SHAP-based Explainable AI (XAI) to achieve robust and interpretable anomaly detection for network traffic in embedded systems.

The paper proposes a system that operationalizes cybersecurity governance frameworks by integrating them with attack-path modeling and Deep Reinforcement Learning to generate practical, resource-const…

The paper proposes ExAI5G, a logic-based explainable AI framework that integrates a Transformer-based IDS with XAI techniques to provide highly accurate and transparent intrusion detection for 5G netw…

The paper proposes a novel semi-automated method to perform continuous threat modeling by inferring the actual system architecture from combined static configuration and dynamic network flow data, sig…

This paper proposes a gap-prioritization framework to bridge the gap between theoretical cyber attack prediction research and practical operational deployment by identifying critical implementation hu…

This paper investigates the practical barriers preventing the trustworthy deployment of AI-driven Cyber Threat Intelligence (CTI) in the highly regulated financial sector, identifying four key socio-t…

The paper proposes MVRAF, a data-driven framework that quantifies vulnerability risk in large-scale cloud infrastructure by integrating multiple attack attributes and analyzing cumulative risk distrib…

The paper proposes a fuzzy modeling framework using subnormal Gaussian fuzzy numbers to prioritize IDS alerts by explicitly incorporating threat severity, detection confidence, and organizational risk…

The paper proposes Operational AI Deployment Assurance (OADA), a governance framework that translates complex AI evaluation metrics and operational uncertainties into actionable, deployment-oriented a…