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Recently, neural network (NN)-based methods, including autoencoders, havebeen proposed for the detection of cyber attacks targeting industrial controlsystems (ICSs). Such detectors are often retrained, using data collected duringsystem operation, to cope with the natural evolution (i.e., concept drift) ofthe monitored signals. However, by exploiting this mechanism, an attacker canfake the signals provided by corrupted sensors at training time and poison thelearning process of the detector such that cyber attacks go undetected at testtime. With this research, we are the first to demonstrate such poisoningattacks on ICS cyber attack online NN detectors. We propose two distinct attackalgorithms, namely, interpolation- and back-gradient based poisoning, anddemonstrate their effectiveness on both synthetic and real-world ICS data. Wealso discuss and analyze some potential mitigation strategies.

Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems