| Title | Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Choi, Hongjun, Lee, Wen-Chuan, Aafer, Yousra, Fei, Fan, Tu, Zhan, Zhang, Xiangyu, Xu, Dongyan, Deng, Xinyan |
| Conference Name | Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-5693-0 |
| Keywords | actuator security, attack and detection, composability, control invariant, cps security, Human Behavior, Metrics, pubcrawl, Resiliency, robotic vehicle |
| Abstract | Robotic vehicles (RVs), such as drones and ground rovers, are a type of cyber-physical systems that operate in the physical world under the control of computing components in the cyber world. Despite RVs' robustness against natural disturbances, cyber or physical attacks against RVs may lead to physical malfunction and subsequently disruption or failure of the vehicles' missions. To avoid or mitigate such consequences, it is essential to develop attack detection techniques for RVs. In this paper, we present a novel attack detection framework to identify external, physical attacks against RVs on the fly by deriving and monitoring Control Invariants (CI). More specifically, we propose a method to extract such invariants by jointly modeling a vehicle's physical properties, its control algorithm and the laws of physics. These invariants are represented in a state-space form, which can then be implemented and inserted into the vehicle's control program binary for runtime invariant check. We apply our CI framework to eleven RVs, including quadrotor, hexarotor, and ground rover, and show that the invariant check can detect three common types of physical attacks - including sensor attack, actuation signal attack, and parameter attack - with very low runtime overhead. |
| URL | http://doi.acm.org/10.1145/3243734.3243752 |
| DOI | 10.1145/3243734.3243752 |
| Citation Key | choi_detecting_2018 |
Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach