Thesis Examination Committee

Prof Ying Ju CHEN, ISOM/HKUST (Chairperson)
Prof Ling SHI, ECE/HKUST (Thesis Supervisor)
Prof Yang SHI, Department of Mechanical Engineering, University of Victoria (External Examiner)
Prof Wai Ho MOW, ECE/HKUST
Prof Ming LIU, ECE/HKUST
Prof Shuhuai YAO, MAE/HKUST 
 

Abstract

Private and secure remote state estimation in the context of cyber-physical systems (CPSs) is studied. Monitoring a physical process, a sensor will forward local state estimates as data packets to a remote estimator over a vulnerable network, which may be attacked by an intelligent adversary. Considering an attacker with different information sets and different destruction abilities, we leverage Markov decision process (MDP) and stochastic game model (or competitive MDP) to develop a systematic quantitative decision framework to protect remote state estimation.
 
We mainly focus on two types of cyber-attacks: eavesdropping and denial-of-service (DoS) attack. Security against cyber threats has been extensively explored in traditional cyber systems, however, it has overlooked the interdependency between the physical components and the cyber domain, which is a particular crucial characteristics in CPSs. First, we study the novel active eavesdropping attack and develop an optimal attack policy for the eavesdropper to improve the eavesdropping performance efficiently and simultaneously avoid being detected. We further address the remote state estimation under DoS attack threats and study the interactive process between the sensor and the attacker via a stochastic game model with symmetric/asymmetric information structure. Moreover, we note the importance of online information in achieving CPS security, and propose a deception-based countermeasure to DoS attacks. Simulation results demonstrate the efficiency and computational simplicity of the proposed defensive policies.