GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection
This addresses the problem of robust DDoS detection for network security, offering a significant improvement but being incremental as it builds on existing adversarial training methods.
The paper tackled the vulnerability of ML-based Network Intrusion Detection Systems (NIDSs) to adversarial attacks, specifically DDoS attacks, by proposing GADoT, a GAN-based adversarial training approach that reduces undetected malicious flows from over 60% to 1.8% or less.
Machine Learning (ML) has proven to be effective in many application domains. However, ML methods can be vulnerable to adversarial attacks, in which an attacker tries to fool the classification/prediction mechanism by crafting the input data. In the case of ML-based Network Intrusion Detection Systems (NIDSs), the attacker might use their knowledge of the intrusion detection logic to generate malicious traffic that remains undetected. One way to solve this issue is to adopt adversarial training, in which the training set is augmented with adversarial traffic samples. This paper presents an adversarial training approach called GADoT, which leverages a Generative Adversarial Network (GAN) to generate adversarial DDoS samples for training. We show that a state-of-the-art NIDS with high accuracy on popular datasets can experience more than 60% undetected malicious flows under adversarial attacks. We then demonstrate how this score drops to 1.8% or less after adversarial training using GADoT.