SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network
  Intrusion Detection
João Vitorino Isabel Praça Eva Maia
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 real 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 future experiments are adequate for a real communication
network.