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 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…

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 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 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 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 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 trust-aware federated hybrid intrusion detection framework using multiple ML models at distributed edge nodes to proactively secure highly connected Intelligent Transport Systems.

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 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.

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…

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.

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…

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 reviews current trends in AI-based cybersecurity, specifically analyzing various AI techniques applied to intrusion detection to provide comparative insights.

The paper proposes an Institutional Coherence Index (ICC) regularization method for federated learning in intrusion detection, demonstrating superior performance by weighting local models based on ins…

SentinelSphere is an AI platform that integrates advanced deep learning for real-time threat detection with an LLM-powered training system to holistically address both technical and human-factor cyber…