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