A cyber-physical system (CPS) is expected to be resilient to more than one type of adversary. In this paper, we consider a CPS that has to satisfy a linear temporal logic (LTL) objective in the presence of two kinds of adversaries. The first adversary has the ability to tamper with inputs to the CPS to influence satisfaction of the LTL objective. The interaction of the CPS with this adversary is modeled as a stochastic game. We synthesize a controller for the CPS to maximize the probability of satisfying the LTL objective under any policy of this adversary. The second adversary is an eavesdropper who can observe labeled trajectories of the CPS generated from the previous step. It could then use this information to launch other kinds of attacks. A labeled trajectory is a sequence of labels, where a label is associated to a state and is linked to the satisfaction of the LTL objective at that state. We use differential privacy to quantify the indistinguishability between states that are related to each other when the eavesdropper sees a labeled trajectory. Two trajectories of equal length will be differentially private if they are differentially private at each state along the respective trajectories. We use a skewed Kantorovich metric to compute distances between probability distributions over states resulting from actions chosen according to policies from related states in order to quantify differential privacy. Moreover, we do this in a manner that does not affect the satisfaction probability of the LTL objective. We validate our approach on a simulation of a UAV that has to satisfy an LTL objective in an adversarial environment.
Multidimensional Resilience for Electrical Power Systems: Systematic Review, Integrated Index, and Validation under Real-World Cyber-Physical Attack Scenarios
Game-Theoretic Resilience Framework for Cyber-Physical Microgrids using Multi-Agent Reinforcement Learning
ABB Ability Innovation Center, Hyderabad（ABB Ability创新中心，海得拉巴）；Indian Institute of Technology Tirupati（印度理工学院特里普蒂学院）；Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad（印度理工学院（ISM）电子工程系，丹巴德）；Indian Institute of Technology (ISM), Dhanbad（印度理工学院（ISM），丹巴德）
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
School of Electrical Engineering, Northeast Electric Power University（东北电力大学电气工程学院）；State Grid XiXian New Area Power Supply Company（国家电网西安新区供电公司）；School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Energy Technology, Aalborg University（奥尔堡大学能源技术研究所）；Illinois Institute of Technology（伊利诺伊理工大学）
Digital Guardians: The Past and The Future of Cyber-Physical Resilience
Purdue University（普渡大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Notre Dame（圣母大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
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）
