The paper introduces a novel approach to Intrusion Detection Systems (IDS), called X-CBA. This approach leverages the advantages of Graph Neural Networks (GNNs) to effectively process network traffic data while…
Continue readingThe research presents a three-stage training model for intrusion detection in edge computing devices. The model uses advanced deep learning techniques and is designed to work effectively within resource-constrained devices….
Continue readingThe research proposes a federated learning framework for intrusion detection in Internet of Vehicles (IOVs) using the CIC-IDS 2017 dataset. The framework uses SMOTE for handling class imbalance, outlier detection…
Continue readingThis study presents HuntGPT, a tool that integrates machine learning-based anomaly detection and Explainable AI (XAI) with Large Language Models (LLMs) for efficient threat hunting. Despite the effectiveness of machine…
Continue readingThe “3D-IDS: Doubly Disentangled Dynamic Intrusion Detection” study developed by a team from the Beijing University of Posts and Telecommunications presents a novel method that aims to enhance network intrusion…
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