Towards Automated Safety Vetting of PLC Code in Real-World Plants presented at IEEESymposium 2019

by Z. Morley Mao, Elaine Shi, Mu Zhang, Yuru Shao, Sibin Mohan, Yikai Lin, Chien-ying Chen, Bin-chou Kao, Yassine Qamsane, Kira Barton, James Moyne,

URL : https://youtu.be/DbG_iejKVRE

Summary : Safety violations in programmable logic controllers (PLCs), caused either by faults or attacks, have recently garnered significant attention. However, prior efforts at PLC code vetting suffer from many drawbacks. Static analyses and verification cause significant false positives and cannot reveal specific runtime contexts. Dynamic analyses and symbolic execution, on the other hand, fail due to their inability to handle real-world PLC programs that are event-driven and timing sensitive. In this paper, we propose VetPLC, a temporal context-aware, program analysis-based approach to produce timed event sequences that can be used for automatic safety vetting. To this end, we (a) perform static program analysis to create timed event causality graphs in order to understand causal relations among events in PLC code and (b) mine temporal invariants from data traces collected in Industrial Control System (ICS) testbeds to quantitatively gauge temporal dependencies that are constrained by machine operations. Our VetPLC prototype has been implemented in 15K lines of code. We evaluate it on 10 real-world scenarios from two different ICS settings. Our experiments show that VetPLC outperforms state-of-the-art techniques and can generate event sequences that can be used to automatically detect hidden safety violations.