| Title | Sensor Deception Attacks Against Initial-State Privacy in Supervisory Control Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Yao, Jingshi, Yin, Xiang, Li, Shaoyuan |
| Conference Name | 2022 IEEE 61st Conference on Decision and Control (CDC) |
| Keywords | Complexity theory, control theory, discrete-event systems, Estimation, Human Behavior, human factors, privacy, pubcrawl, resilience, Resiliency, Scalability, supervisory control |
| Abstract | This paper investigates the problem of synthesizing sensor deception attackers against privacy in the context of supervisory control of discrete-event systems (DES). We consider a plant controlled by a supervisor, which is subject to sensor deception attacks. Specifically, we consider an active attacker that can tamper with the observations received by the supervisor. The privacy requirement of the supervisory control system is to maintain initial-state opacity, i.e., it does not want to reveal the fact that it was initiated from a secret state during its operation. On the other hand, the attacker aims to deceive the supervisor, by tampering with its observations, such that initial-state opacity is violated due to incorrect control actions. We investigate from the attacker's point of view by presenting an effective approach for synthesizing sensor attack strategies threatening the privacy of the system. To this end, we propose the All Attack Structure (AAS) that records state estimates for both the supervisor and the attacker. This structure serves as a basis for synthesizing a sensor attack strategy. We also discuss how to simplify the synthesis complexity by leveraging the structural properties. A running academic example is provided to illustrate the synthesis procedure. |
| Notes | ISSN: 2576-2370 |
| DOI | 10.1109/CDC51059.2022.9992694 |
| Citation Key | yao_sensor_2022 |
Sensor Deception Attacks Against Initial-State Privacy in Supervisory Control Systems