Automated Runtime Mitigation for Misconfiguration Vulnerabilities in Industrial Control Systems


연구 분야: Infrastructure



학회: RAID '22: Proceedings of the 25th International Symposium on Research in Attacks, Intrusions and Defenses


초록

Cyber-physical industrial control systems (ICS) commonly implement configuration parameters that can be remotely tuned by human-machine interfaces (HMI) at runtime. These parameters directly control the behaviors of ICSs thus they can be exploited by attackers to compromise the safety of ICSs, proved by real-world attacks worldwide. However, existing anomaly detection methods, which mostly focus on the programmable logic controller (PLC) programs or sensor signals, lack a comprehensive analysis of configuration’s impact on the entire system and thus cannot effectively detect improper parameters. A tool that automatically analyzes complicated control logic to determine the safety of configuration is absent. To fill this gap, we design SmtConf, a verification-based framework for detecting and mitigating improper parameters in ICSs at runtime. To understand the impact of configuration parameters on complicated control logic, we design a symbolic formal model representing behaviors of the ICS under any possible configuration parameters. Based on the model, SmtConf works as a monitoring system that detects safety violations in real-time when the improper configuration is injected. To further assist developers to determine the safe configuration, SmtConf recommends safe configuration parameters by solving an optimization problem. In 18 test cases collected from two production-level ICS testbeds, SmtConf detects all true violations caused by improper parameters in 0.41 seconds and correctly repairs the ICS with recommended safe parameters in 0.45 seconds.


Author Profile
Zhuoqing Morley Mao

University of Michigan United States of America

United States
Author Profile
Mu Zhang

University of Utah United States of America

United States
Author Profile
Shawn

University of Michigan United States of America

United States

📄 논문 정보

발행 연도 2022년
인용수 4
출판 국가 United States
사이트 ACM
좋아요 수 0

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