Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection

Key points

• 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.1
• 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.1
• The proposed methodology further enables targeted mitigation via automated data sanitization or neuron pruning.1
• 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.1

Sources (1)

• [1]Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:17 AM

  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.

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