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…

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.

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 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…

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 evaluates AI's effectiveness in detecting network intrusions and cryptographic side-channel leakage, finding high accuracy in stable environments but performance degradation with novel traff…

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…

The paper proposes a declarative, autonomous, self-protecting framework for securing complex 5G/6G networks by leveraging a standardized security ontology and automated graph reasoning to neutralize l…

This paper introduces an attribution-driven analysis of encoder-based Large Language Models (LLMs) for network intrusion detection, demonstrating that the models make decisions based on meaningful tra…

The paper introduces Constraint-Guided Semantic Testing (ConSeT), a novel framework that systematically finds critical, pre-authentication vulnerabilities in 5G User Equipment (UE) by exploiting seman…

This paper experimentally demonstrates the high detection performance of machine learning-based intrusion detection systems for identifying cyberattacks targeting OPC UA applications running over priv…

The paper proposes a confidence-aware, multi-layered Cloud-IDS pipeline that integrates adaptive Q-Learning, Chroma memory, and LLM semantic analysis to enhance detection accuracy and reduce reliance…

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…

The paper introduces AICCE, an AI-driven engine that uses generative systems and dual-architecture reasoning to accurately verify IPv6 compliance, overcoming the limitations of traditional rule-based…

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 introduces EXTree, a novel structure for Attribute-based Access Control (ABAC) policies that optimizes for both fast evaluation and human-understandable explanations when access is denied.

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…

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.