Intrinsic Weaknesses of IDSs to Malicious Adversarial Attacks and Their Mitigation

Intrusion Detection Systems (IDS) are essential tools to protect network security from malicious traffic. IDS have recently made significant advancements in their detection capabilities through deep learning algorithms compared to conventional approaches. However, these algorithms are vulnerable to meta-attacks, also known as adversarial evasion attacks, which are attacks that improve already existing attacks, specifically their ability to evade detection. Deep learning-based IDS, in particular, are particularly susceptible to adversarial evasion attacks that use Generative Adversarial Networks (GAN). Nonetheless, well-known strategies have been proposed to cope with this threat. However, these countermeasures lack robustness and predictability, and their performance can be either remarkable or poor. Such robustness issues have been identified even without adversarial evasion attacks, and mitigation strategies have been provided. This paper identifies and formalizes threats to the robustness of IDSs against adversarial evasion attacks. These threats are enabled by flaws in the dataset’s structure and content rather than its representativeness. In addition, we propose a method for enhancing the performance of adversarial training by directing it to focus on the best evasion candidates samples within a dataset. We find that GAN adversarial attack evasion capabilities are significantly reduced when our method is used to strengthen the IDS.