The paper proposes MLDAS, a framework that dynamically selects the optimal Machine Learning algorithm for Intrusion Detection Systems within Software-Defined Networking to enhance adaptive network sec…

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 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…

This paper proposes an improved CNN-LSTM model for IoT intrusion detection, achieving high accuracy by combining spatial and temporal feature learning from network traffic.

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…

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…

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 study assesses the generalization capability of supervised machine learning models for intrusion detection using UNSW-NB15 and TON_IoT, finding a significant performance drop when models are teste…

NetVAD proposes a novel, identifier-free Variational Autoencoder that leverages frozen Foundation Models to achieve highly competitive unsupervised performance for zero-day intrusion detection.

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 introduces HIDBench, a new benchmark for evaluating LLMs' ability to perform host-based intrusion detection using complex, noisy system logs, finding that model performance degrades signific…

The paper proposes a robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly handles adversarial contamination and temporal drift in unlabeled network traffic,…

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 introduces ESPRESSO, a deep learning model that significantly improves the detection of sophisticated stepping-stone intrusions by correlating network flows across multiple relay hosts.

This paper proposes a comprehensive framework for network intrusion detection using unified multi-modal datasets and evaluates advanced adversarial learning methods for generating high-fidelity synthe…

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 a novel Retrieval-Augmented Generation (RAG) framework utilizing Large Language Models (LLMs) for real-time, intelligent detection and mitigation of evasive Carpet-Bombing DDoS atta…

The paper proposes PROVFUSION, a multi-view fusion framework that integrates anomaly signals from attribute, structure, and causality views to overcome the limitations of single node- or edge-centric…