A False Sense of Security? Revisiting the State of Machine Learning-Based Industrial Intrusion Detection. (arXiv:2205.09199v1 [cs.CR])

Anomaly-based intrusion detection promises to detect novel or unknown attacks
on industrial control systems by modeling expected system behavior and raising
corresponding alarms for any deviations.As manually creating these behavioral
models is tedious and error-prone, research focuses on machine learning to
train them automatically, achieving detection rates upwards of 99%. However,
these approaches are typically trained not only on benign traffic but also on
attacks and then evaluated against the same type of attack used for training.
Hence, their actual, …

detection industrial intrusion intrusion detection machine machine learning security state