Modern Cyber-Physical Systems (CPSs) are often designed as networked, software-based controller implementations which have been found to be vulnerable to network-level and physical level attacks. A number of research works have proposed CPS-specific attack detection schemes as well as techniques for attack resilient controller design. However, such schemes also incur platform-level overheads. In this regard, some recent works have leveraged the use of skips in control execution to enhance the resilience of a CPS against false data injection (FDI) attacks.
In this paper, we provide an analytical discussion on when and how skipping a control execution can improve the resilience of the system against FDI attacks while maintaining the control performance requirement. We also propose a methodology to synthesize such optimal control execution patterns. To the best of our knowledge, no previous work has provided any quantitative analysis about the trade-off between attack resilience and control performance for such aperiodic control execution. Finally, we evaluate the proposed method on several safety-critical CPS benchmarks.
Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems
Departmento de Ingenier\'ia El\'ectrica, Universidad de Concepci\'on, Concepci\'on, Chile (e-mail: )；Department of Electrical \& Computer Engineering, University of Alberta, Edmonton, T6G 1H9, Alberta, AB, Canada (e-mail: )；Department of Chemical；Biological Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada ( )；Department of Chemical \& Materials Engineering, University of Alberta, Edmonton, T6G 1H9, Alberta, AB, Canada (e-mail: )
Performance and Resilience of Cyber-Physical Control Systems with Reactive Attack Mitigation
University of Warwick（华威大学）；Oracle India Pvt. Ltd.（甲骨文印度私人有限公司）；GovTech Singapore（新加坡政府科技局）；Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）
This paper examines how moving target defences (MTD) implemented in power systems can be countered by unsupervised learning-based false data injection (FDI) attack and how MTD can be combined with physical watermarking to enhance the system resilience. A novel intelligent attack, which incorporates density-based spatial clustering and dimensionality reduction, is developed and shown to be effective in maintaining stealth in the presence of traditional MTD strategies. In resisting this new type of attack, a novel implementation of MTD combining with physical watermarking is proposed by adding G…
