An Approach to Improve the Robustness of Machine Learning based Intrusion Detection System Models Against the Carlini-Wagner Attack

Machine Learning (ML) techniques have been applied over the past two decades to improve the abilities of Intrusion Detection Systems (IDSs). Over time, several enhancements have been implemented to help the ML-based IDS models tackle the ever-evolving attack behaviors. However, recent works reveal that ML models are vulnerable to adversarial perturbations. With the increasing volumes of data passing through systems, defeating adversarial attacks has become a significant challenge. Recent research suggests that Generative Adversarial Networks (GANs) possess a good potential in creating adversarial samples and tackling them, playing well on both offense and defense teams. With a motive to improve the resistance of ML-based IDS models against a powerful white-box evasion attack technique, the Carlini-Wagner, we propose a GAN-based defensive approach and evaluate it with the CSE-CIC-IDS2018 dataset. The paper presents preliminary evaluation results and discusses the direction in which we want to continue the work.