| Title | Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Zhang, Lin, Chen, Xin, Kong, Fanxin, Cardenas, Alvaro A. |
| Conference Name | 2020 IEEE Real-Time Systems Symposium (RTSS) |
| Date Published | dec |
| Keywords | Analytical models, Computational modeling, Cyber-physical systems, Linear systems, pubcrawl, Real-time, Real-time Systems, recovery, resilience, Resiliency, Safety, Scalability, security, sensor attacks, System recovery |
| Abstract | Attack detection and recovery are fundamental elements for the operation of safe and resilient cyber-physical systems. Most of the literature focuses on attack-detection, while leaving attack-recovery as an open problem. In this paper, we propose novel attack-recovery control for securing cyber-physical systems. Our recovery control consists of new concepts required for a safe response to attacks, which includes the removal of poisoned data, the estimation of the current state, a prediction of the reachable states, and the online design of a new controller to recover the system. The synthesis of such recovery controllers for cyber-physical systems has barely investigated so far. To fill this void, we present a formal method-based approach to online compute a recovery control sequence that steers a system under an ongoing sensor attack from the current state to a target state such that no unsafe state is reachable on the way. The method solves a reach-avoid problem on a Linear Time-Invariant (LTI) model with the consideration of an error bound $e$ $\geq$ 0. The obtained recovery control is guaranteed to work on the original system if the behavioral difference between the LTI model and the system's plant dynamics is not larger than $e$. Since a recovery control should be obtained and applied at the runtime of the system, in order to keep its computational time cost as low as possible, our approach firstly builds a linear programming restriction with the accordingly constrained safety and target specifications for the given reach-avoid problem, and then uses a linear programming solver to find a solution. To demonstrate the effectiveness of our method, we provide (a) the comparison to the previous work over 5 system models under 3 sensor attack scenarios: modification, delay, and reply; (b) a scalability analysis based on a scalable model to evaluate the performance of our method on large-scale systems. |
| DOI | 10.1109/RTSS49844.2020.00028 |
| Citation Key | zhang_real-time_2020 |
Real-Time Attack-Recovery for Cyber-Physical Systems Using Linear Approximations