Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 12, Pages 9934-9947Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3164723
Keywords
Control systems; Poles and towers; Ethanol; Liquids; Process control; Internet of Things; Correlation; Control invariants; intrusion detection system (IDS); programmable logic controllers (PLCs)
Categories
Funding
- Science and Technology Innovation 2030 Program [2018AAA0101605]
- National Natural Science Foundation of China [61833015, 61903328]
- Zhejiang Provincial Natural Science Foundation [LZ22F030010]
- UC Denver
Ask authors/readers for more resources
PLC-Sleuth is an intrusion detection/localization system for PLCs based on control invariants and control graphs, which has shown high accuracy and effectiveness in detecting and localizing intrusions during testing.
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 parts per thousand 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available