Detecting PLC Intrusions Using Control Invariants

Abstract

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we design PLC-Sleuth , a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants—i.e., the correlations between sensor readings and the concomitantly triggered PLC commands—that exist pervasively in all control systems. Specifically, taking the system’s supervisory control and data acquisition log as input, PLC-Sleuth abstracts/identifies the system’s control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluated PLC-Sleuth using both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show that PLC-Sleuth can: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE; 2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE; and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE.