SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their experiments are adequate for a real communication network.

Automated summary

Machine Learning has great value in Network Intrusion Detection, but it is vulnerable to adversarial attacks. Advances in adversarial learning have allowed the generation of realistic examples for ML development and deployment with real network traffic flows. However, challenges and issues still exist when it comes to using adversarial ML in Network Intrusion Detection.