IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detection

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 are under research. In this paper, a framework of the generative adversarial networks, called IDSGAN, is proposed to generate the adversarial malicious traffic records aiming to attack intrusion detection systems by deceiving and evading the detection. Given that the internal structure of the detection system is unknown to attackers, the adversarial attack examples perform the black-box attacks against the detection system. IDSGAN leverages a generator to transform original malicious traffic records into adversarial malicious ones. A discriminator classifies traffic examples and learns the black-box detection system. More significantly, to guarantee the validity of the intrusion, only part of the nonfunctional features are modified in attack traffic. Based on the tests to the dataset NSL-KDD, the feasibility of the model is indicated by attacking multiple kinds of the detection system models with different attack categories, achieving the excellent results. Moreover, the robustness of IDSGAN is verified by changing the amount of the modified features.