Bringing to Light: Adversarial Poisoning Detection for ML-Based IDS in Software-Defined Networks

Machine learning (ML)-based network intrusion detection systems (NIDS) have become a prospective approach to efficiently protect network communications. However, ML models can be exploited by adversarial poisonings, like Random Label Manipulation (RLM), which can compromise multi-controller software-defined network (MSDN) operations. In this paper, we develop the Trans-controller Adversarial Perturbation Detection (TAPD) framework for NIDS for MSDNs. The detection framework takes advantage of the MSDN architecture and focuses on periodic transference of ML-based NIDS models across the SDN controllers in the topology, and validates the models using local datasets to calculate error rates. We demonstrate the efficacy of this framework in detecting RLM attacks in an MSDN setup. Results indicate efficient detection performance by the TAPD framework in determining the presence of RLM attacks and the localization of the compromised controllers. We find that the framework works well even when there is a significant number of compromised agents. However, the performance begins to deteriorate when more than 40% of the SDN controllers have become compromised.