mlsanityData to 14 Oct 2026

Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection

arXiv 2610.19766 ↗First posted 14 Oct 2026cs.CR cs.LGcited by 0

On the map: LLM safety and security → Backdoor and data poisoning attacks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security. Builds on 8 of its 15 references that are in the map. First posted on arXiv 14 Oct 2026.

Builds on

8 of 15 references.

References · 8

  1. 2020Knowledge Distillation: A Surveycited by 1,603
  2. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,106
  3. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  4. 2019Label-Consistent Backdoor Attackscited by 296
  5. 2020Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defensescited by 180
  6. 2022CrowdGuard: Federated Backdoor Detection in Federated Learningcited by 27
  7. 2023Backdoor Federated Learning by Poisoning Backdoor-Critical Layerscited by 18
  8. 2023Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection Systemcited by 3

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  1. 2023Poisoning Network Flow Classifiers
  2. 2022Towards A Proactive ML Approach for Detecting Backdoor Poison Samples
  3. 2021Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
  4. 2023Demystifying Poisoning Backdoor Attacks from a Statistical Perspective
  5. 2022AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
  6. 2026Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
  7. 2026CSC: Turning the Adversary's Poison against Itself
  8. 2024Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization