Adversarial Attacks on Network Intrusion Detection Systems Using Flow Containers

Abstract

Abstract This paper studies adversarial attacks on network intrusion detection systems (IDSs) based on deep or machine learning algorithms. Adversarial attacks on network IDSs must maintain the functional logic of the attack flow. To prevent the produced adversarial examples from violating the attack behavior, most solutions define some limited modification actions. The result limits the production of adversarial examples, and the produced adversarial examples are not guaranteed to find the attack packets. This paper proposes the concept of flow containers to model packets in a flow. Then, we propose a generative adversarial network framework with dual adversarial training to train the generator to produce adversarial flow containers. Flow containers can correlate attack packets and feature vectors of attack flows. We test the evasion rate of the produced adversarial examples using 12 deep and machine learning algorithms. For experiments on the CTU42 data set, the proposed adversarial examples have the highest evasion rates among all 12 classifiers, with the highest evasion rate as high as 1.00. For experiments on the CIC-IDS2017 data set, the proposed adversarial examples have the highest evasion rate among the five classifiers, and the highest evasion rate is also up to 1.00.