IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

By Zilong Lin, Yong-yu Shi, Zhi Xue. Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018

353 citations recorded by the source corpus.

As an important tool in security, the intrusion detection system bears the responsibility of the defense to network attacks performed by malicious traffic. Nowadays, with the help of machine learning algorithms, the intrusion detection system develops rapidly. However, the robustness of this system is questionable when it faces the adversarial attacks. To improve the detection system, more potential attack approaches should be researched. In this paper, a framework of the generative adversarial networks, IDSGAN, is proposed to generate the adversarial attacks, which can deceive and evade the intrusion detection system. Considering that the internal structure of the detection system is unknown to attackers, adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic into adversarial malicious traffic. A discriminator classifies traffic examples and simulates the black-box detection system. More significantly, we only modify part of the attacks' nonfunctional features to guarantee the validity of the intrusion. Based on the dataset NSL-KDD, the feasibility of the model is demonstrated to attack many detection systems with different attacks and the excellent results are achieved. Moreover, the robustness of IDSGAN is verified by changing the amount of the unmodified features.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1875e0822933a8e3bc76d269266af804c54b0f10

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Citations in the corpus

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, background): “Lin et al. [105] assume a GB threat model, where the attacker knows the features being extracted but treats the classiﬁer as an oracle.”