A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems

Benyamin Tafreshian, Shengzhi Zhang
2/21/2025
cs.CRcs.LG

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Abstract

As cyberattacks become increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) are critical for modern network security. Traditional signature-based NIDS are inadequate against zero-day and evolving attacks. In response, machine learning (ML)-based NIDS have emerged as promising solutions; however, they are vulnerable to adversarial evasion attacks that subtly manipulate network traffic to bypass detection. To address this vulnerability, we propose a novel defensive framework that enhances the robustness of ML-based NIDS by simultaneously integrating adversarial training, dataset balancing techniques, advanced feature engineering, ensemble learning, and extensive model fine-tuning. We validate our framework using the NSL-KDD and UNSW-NB15 datasets. Experimental results show, on average, a 35% increase in detection accuracy and a 12.5% reduction in false positives compared to baseline models, particularly under adversarial conditions. The proposed defense against adversarial attacks significantly advances the practical deployment of robust ML-based NIDS in real-world networks.

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Cite this paper

@article{tafreshian2025defensive,
  title  = {A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems},
  author = {Benyamin Tafreshian and Shengzhi Zhang},
  year   = {2025},
  eprint = {2502.15561},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.15561v1}
}

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