TY - GEN
T1 - A Framework for Joint Attack Detection and Control Under False Data Injection
AU - Niu, Luyao
AU - Clark, Andrew
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In this work, we consider an LTI system with a Kalman filter, detector, and Linear Quadratic Gaussian (LQG) controller under false data injection attack. The interaction between the controller and adversary is captured by a Stackelberg game, in which the controller is the leader and the adversary is the follower. We propose a framework under which the system chooses time-varying detection thresholds to reduce the effectiveness of the attack and enhance the control performance. We model the impact of the detector as a switching signal, resulting in a switched linear system. A closed form solution for the optimal attack is first computed using the proposed framework, as the best response to any detection threshold. We then present a convex program to compute the optimal detection threshold. Our approach is evaluated using a numerical case study.
AB - In this work, we consider an LTI system with a Kalman filter, detector, and Linear Quadratic Gaussian (LQG) controller under false data injection attack. The interaction between the controller and adversary is captured by a Stackelberg game, in which the controller is the leader and the adversary is the follower. We propose a framework under which the system chooses time-varying detection thresholds to reduce the effectiveness of the attack and enhance the control performance. We model the impact of the detector as a switching signal, resulting in a switched linear system. A closed form solution for the optimal attack is first computed using the proposed framework, as the best response to any detection threshold. We then present a convex program to compute the optimal detection threshold. Our approach is evaluated using a numerical case study.
KW - Control system
KW - Detection threshold
KW - False data injection attacks
KW - K-L divergence
KW - LQG control
KW - Stealthiness
UR - https://www.scopus.com/pages/publications/85076399604
U2 - 10.1007/978-3-030-32430-8_21
DO - 10.1007/978-3-030-32430-8_21
M3 - Conference contribution
AN - SCOPUS:85076399604
SN - 9783030324292
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 352
EP - 363
BT - Decision and Game Theory for Security - 10th International Conference, GameSec 2019, Proceedings
A2 - Alpcan, Tansu
A2 - Vorobeychik, Yevgeniy
A2 - Baras, John S.
A2 - Dán, György
PB - Springer
T2 - 10th International Conference on Decision and Game Theory for Security, GameSec 2019
Y2 - 30 October 2019 through 1 November 2019
ER -