Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
This research addresses a critical security vulnerability for operators of industrial control systems, as it shows how an attacker can compromise the reliability of cyber attack detectors.
This paper demonstrates the first poisoning attacks on neural network-based cyber attack detectors for industrial control systems (ICSs). The authors propose two attack algorithms, interpolation- and back-gradient based poisoning, and show their effectiveness on both synthetic and real-world ICS data, causing cyber attacks to go undetected.
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.