Paper Info
Title
Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach.
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.
Year
DOI
Venue
2018
10.1145/3243734.3243752
ACM Conference on Computer and Communications Security
Keywords
Field
DocType
CPS Security, Robotic Vehicle, Control Invariant, Attack and Detection
Control algorithm,Computer security,Computer science,On the fly,Robustness (computer science),Real-time computing,Drone,Invariant (mathematics),Physical law,Binary number
Conference
ISBN
Citations 
PageRank 
978-1-4503-5693-0
15
0.59
References 
Authors
37
8
Name
Order
Citations
PageRank
Hongjun Choi1274.87
Wen-Chuan Lee220320.36
Yousra Aafer326413.36
Fan Fei4161.74
Zhan Tu5286.22
Xiangyu Zhang62857151.00
Dongyan Xu73158212.56
Xinyan Deng823843.88