The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

The paper proposes XAI-SOH-FL, an enhanced Federated Learning framework that improves IoT intrusion detection by integrating adaptive aggregation and explainable AI, achieving high accuracy and interp…

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…

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…

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…

UNAD+ is an enhanced, explainable hybrid framework that effectively detects unknown zero-day network attacks by combining unsupervised ensemble methods with supervised refinement and post hoc explaina…

This paper proposes using Age of Information (AoI)-guided client selection to improve the timeliness and robustness of federated intrusion detection in cloud-edge environments, achieving significant r…

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 introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

CLAD is a federated learning framework that jointly performs anomaly detection and attack classification in heterogeneous IoT environments by combining clustered learning with a dual-mode architecture…

EdgeDetect is a communication-efficient and privacy-preserving federated intrusion detection system that uses gradient binarization and homomorphic encryption to significantly reduce bandwidth usage w…

The paper proposes EFAH-ZTM, an explainable federated framework that uses hypergraphs and autoencoders to perform robust zero-trust micro-segmentation in complex IIoT networks.

This paper proposes and evaluates a federated deep learning framework using autoencoders for lightweight, privacy-preserving, and scalable real-time anomaly detection in resource-constrained IoT netwo…

This paper proposes an Explainable AI (XAI)-driven framework using XGBoost and SHAP to enhance cyber risk analytics and model reliability for intelligent governance of U.S. critical infrastructure.

This paper develops an explainable and deployable machine learning system for highly accurate phishing detection across diverse, heterogeneous datasets, achieving up to 99.78% accuracy using transform…

MA-IDS proposes a Multi-Agent RAG framework that uses LLMs and a self-building Experience Library to achieve explainable and self-improving intrusion detection for resource-constrained IoT networks.

This paper enhances an existing autonomous online Intrusion Detection System (AOC-IDS) for IoT by addressing class imbalance, pseudo-label reliability, and computational overhead, achieving significan…

This paper systematically evaluates Provenance-based Intrusion Detection Systems (PIDSes) in real industrial scenarios, revealing that existing systems struggle with data heterogeneity, advanced attac…

The paper proposes S2-WEF, a novel detection method that simulates potential global-model-based attacks to dynamically identify free-riding clients in Federated Learning, achieving high robustness aga…