Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations. (arXiv:2202.11917v1 [cs.CR])

Methods from machine learning are being applied to design Industrial Control
Systems resilient to cyber-attacks. Such methods focus on two major areas: the
detection of intrusions at the network-level using the information acquired
through network packets, and detection of anomalies at the physical process
level using data that represents the physical behavior of the system. This
survey focuses on four types of methods from machine learning in use for
intrusion and anomaly detection, namely, supervised, semi-supervised,
unsupervised, and reinforcement learning. …

applications challenges control detection industrial industrial control systems intrusion detection machine machine learning systems