Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems

• Published in: IEEE transactions on dependable and secure computing Vol. 21; no. 1; pp. 153 - 167
• Main Authors: Yan, Haonan, Li, Xiaoguang, Zhang, Wenjing, Wang, Rui, Li, Hui, Zhao, Xingwen, Li, Fenghua, Lin, Xiaodong
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.01.2024; IEEE Computer Society
• Subjects: Adversarial traffic example; black-box evasion attack; Closed box; Cybersecurity; Deep learning; Detectors; Feature extraction; Glass box; Intrusion detection systems; Machine learning; model extraction attack; network intrusion detection system; Optimization; Protocols; System effectiveness; Training; transfer attack
• ISSN: 1545-5971; 1941-0018
• DOI: 10.1109/TDSC.2023.3247585

Network intrusion detection systems (IDS) are often considered effective to thwart cyber attacks. Currently, state-of-the-art (SOTA) IDSs are mainly based on machine learning (ML) including deep learning (DL) models, which suffer from their own security issues, especially evasion attacks by using adversarial examples. However, previous studies mostly focus on extracted features rather than the traffic sample itself, and/or assume that the adversary knows the information of the target model more or less, which severely restricts attack feasibility in practice. In this paper, we re-investigate this problem in a more realistic label-only black-box scenario and propose a practical evasion attack strategy to solve the above limitations. In this newly considered case that the adversary morphs the traffic sample and only obtains the results accepted or rejected without other knowledge, we successfully leverage the model extraction and transfer attack to evade the detection. The entire attack strategy is automated and a comprehensive evaluation is performed. Final results show that the proposed strategy effectively evades seven typical ML-based IDSs and one SOTA DL-based IDS with an average success rate of over <inline-formula><tex-math notation="LaTeX">75\%</tex-math> <mml:math><mml:mrow><mml:mn>75</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="li-ieq1-3247585.gif"/> </inline-formula>. We also discuss the corresponding countermeasures against our attack, which finally highlight the need for effective defenses against our attack.