PLC-Sleuth: Detecting and Localizing PLC Intrusions Using Control Invariants


Zeyu Yang, Zhejiang University; Liang He, University of Colorado Denver; Peng Cheng and Jiming Chen, Zhejiang University; David K.Y. Yau, Singapore University of Technology and Design; Linkang Du, Zhejiang University


Programmable Logic Controllers (PLCs) are the ground of control systems, which are however, vulnerable to a variety of cyber attacks, especially for networked control systems. To mitigate this issue, we design PLC-Sleuth, a novel non-invasive intrusion detection/localization system for PLCs, grounding 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 prototype of Secure Ethanol Distillation System (SEDS) and a realistically simulated Tennessee Eastman (TE) process.

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@inproceedings {259689,
author = {Zeyu Yang and Liang He and Peng Cheng and Jiming Chen and David K.Y. Yau and Linkang Du},
title = {PLC-Sleuth: Detecting and Localizing {PLC} Intrusions Using Control Invariants},
booktitle = {23rd International Symposium on Research in Attacks, Intrusions and Defenses ({RAID} 2020)},
year = {2020},
isbn = {978-1-939133-18-2},
address = {San Sebastian},
pages = {333--348},
url = {},
publisher = {{USENIX} Association},
month = oct,