Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection
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 wi...
| Authors | Florian Spelter, Johannes F. Loevenich, Tobias Hürten et al. |
|---|---|
| Primary category | cs.CR |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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