Apollon: A robust defense system against Adversarial Machine Learning attacks in Intrusion Detection Systems

The rise of Adversarial Machine Learning (AML) attacks is presenting a significant challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address this issue, we introduce Apollon, a novel defence system that can protect IDS against AML attacks. Apollon utilizes a diverse set of classifiers to identify intrusions and employs Multi-Armed Bandits (MAB) with Thompson sampling to dynamically select the optimal classifier or ensemble of classifiers for each input. This approach enables Apollon to prevent attackers from learning the IDS behaviour and generating adversarial examples that can evade the IDS detection. We evaluate Apollon on several of the most popular and recent datasets, and show that it can successfully detect attacks without compromising its performance on traditional network traffic. Our results suggest that Apollon is a robust defence system against AML attacks in IDS.