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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 increasingly vulnerable to adversarial evasion attacks and concept drift caused by adaptive attackers. Existing ensemble-based defenses optimize for instantaneous accuracy without incorporating drift signals, while drift…
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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 extracted from collaborators without centralizing the sensitive data, have become essential. Specifically, decentralized FL (DFL)-based IDSs are increasingly utilized to enhance robustness and eliminate the single…
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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 process against malicious data injection remains underexplored. This study presents a vulnerability analysis of SL against data-level poisoning, including label-flipping and GAN-generated adversarial sample injection. We…
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Android malware is increasingly becoming a serious security threat to users as the popularity of mobile phones continues to rise. The application of machine learning models for classifying Android malware has been widely used in related studies. However, machine learning models can outperform techniques utilizing Generative Adversarial Networks (GANs). This…
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MAF-LLM: A Multi-Agent Framework Based on Large Language Models for Automated Ransomware Memory Forensics
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Federated learning (FL) enables collaborative Intrusion Detection Systems (IDS) across distributed Internet of Things (IoT) networks without sharing raw data. However, its openness exposes it to model poisoning and backdoor attacks, where malicious clients manipulate updates to corrupt the global model. Detecting such threats remains difficult under non-independent and identically…
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Web Application Firewalls (WAFs) are crucial in mitigating web-based threats such as SQLi and XSS, yet the evolving complexity of WAF detection mechanisms poses significant challenges for penetration testing (pentest) tools. Existing ML- and RL-based fuzzers often suffer from three main limitations: (1) reliance on static training datasets, making them…
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