Evading Machine Learning Based Network Intrusion Detection Systems with GANs
Bolor‐Erdene Zolbayar, Ryan Sheatsley, Patrick Drew McDaniel, Mike Weisman · 2021
Machine learning (ML) is fundamentally changing our way of life with the recent availability of high computational power and big data. Emerging ML-based techniques of network intrusion detection systems (NIDS) can detect complex cyberattacks, undetectable by conventional techniques. In this chapter, we evaluate the threat of a generative adversarial networks (GAN) aided-attack on these systems. In our threat model, an adversarial attacker, given access to a training data of the NIDS, adds a minimal perturbation to the feature values of attack traffic to change the DNN's prediction from “malicious” to “benign.” We evaluate our attack algorithm against two state-of-the-art DNN models as well as our own well-trained DNN model achieving nearly 100% success rates in whitebox setting. We also show that adversarial traffic crafted on these three DNN models also transfer and fool the NIDS models trained with classic ML algorithms with a high accuracy: logistic regression, support vector machine, decision tree and k -nearest neighbors. Our work shows that ML-based NIDS are vulnerable to adversarial network traffic crafted by our GAN-based attack algorithm.