arXiv:2610.19766 · v2
Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
摘要
Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on large-scale training data introduces a critical vulnerability to stealthy poisoning attacks. We demonstrate that latent backdoors can be successfully implanted in flow-based IDS models by poisoning as little as 0.001% of the training data, maintaining near-nominal performance on clean inputs while ensuring high attack success rates. To address this threat, we propose a diagnostic-first methodology that localizes trigger-relevant features through constrained perturbation analysis and detects poisoning-induced behavioral drift by comparison with a distilled baseline model. The proposed methodology further enables targeted mitigation via automated data sanitization or neuron pruning. Our findings expose the operational risks of near-undetectable backdoors and provide a scalable approach to analyzing, quantifying, and mitigating backdoor exposure in AI-based IDS.
作者备注：6 pages, 5 figures, 4 tables. Published in the proceedings of the 2026 IEEE International Conference on Cyber Security and Resilience (CSR)
期刊信息：2026 IEEE International Conference on Cyber Security and Resilience (CSR), Lisbon, Portugal, 2026, pp. 113-118