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A Class-incremental and Few-shot learning model for Intrusion detection under Concept drift

Network Intrusion Detection Systems (NIDS) based on machine learning must evolve after deployment to detect emerging threats very soon with a few labeled samples,…

Assessing Evasion and Behavioral Trade-offs of Obfuscation-Conditioned LLMs in PowerShell Attack Synthesis

S-AECR: Word Intelligence and Explanation Strategy for Cyber Threat Intelligence Extraction and Mapping

MORPH-IDS: A Context-Driven Multi-Agent Reinforcement Learning Framework for Drift-Aware Moving Target Defense in Adversarial-Robust Intrusion Detection

In the rapidly evolving cybersecurity landscape, Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) have become essential for detecting sophisticated threats, yet they are…

XDFC-IDS: An Explainable Decentralized Federated Class-Incremental Fusion Framework for Intrusion Detection

With the widespread adoption of IoT and edge computing, federated learning (FL)-based intrusion detection systems (IDSs), which enable privacy-preserving, cost-effective training by fusing knowledge…

Poisoning the Swarm: Evaluating Data-Level Vulnerabilities in Decentralized Internet of Medical Things-enabled Healthcare Networks

Swarm Learning (SL) integrates federated learning and blockchain to support decentralized privacy-preserving learning for smart healthcare. However, the resilience of its learning and aggregation…

Enhanced android malware classification using multi machine learning models and generative adversarial network

Android malware is increasingly becoming a serious security threat to users as the popularity of mobile phones continues to rise. The application of machine…

Active Digital Twin Verification for Robust Federated Learning in IoT Intrusion Detection

MAF-LLM: A Multi-Agent Framework Based on Large Language Models for Automated Ransomware Memory Forensics

MAF-LLM: A Multi-Agent Framework Based on Large Language Models for Automated Ransomware Memory Forensics