TY - GEN
T1 - A secure control learning framework for cyber-physical systems under sensor attacks
AU - Zhou, Yuanqiang
AU - Vamvoudakis, Kyriakos G.
AU - Haddad, Wassim M.
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2019 American Automatic Control Council.
PY - 2019/7
Y1 - 2019/7
N2 - In this paper, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor attacks. Specifically, we use several observer-based estimators to detect the attacks while also introducing a threat detection level function. We then solve the underlying joint state estimation and attack mitigation problems by using a reinforcement learning algorithm. Finally, an illustrative numerical example is provided to show the efficacy of the proposed framework.
AB - In this paper, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor attacks. Specifically, we use several observer-based estimators to detect the attacks while also introducing a threat detection level function. We then solve the underlying joint state estimation and attack mitigation problems by using a reinforcement learning algorithm. Finally, an illustrative numerical example is provided to show the efficacy of the proposed framework.
KW - Attack estimation
KW - Cyber-physical security
KW - Differential games
KW - Mitigation
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85072288261&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072288261&partnerID=8YFLogxK
U2 - 10.23919/acc.2019.8814659
DO - 10.23919/acc.2019.8814659
M3 - Conference contribution
AN - SCOPUS:85072288261
T3 - Proceedings of the American Control Conference
SP - 4280
EP - 4285
BT - 2019 American Control Conference, ACC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 American Control Conference, ACC 2019
Y2 - 10 July 2019 through 12 July 2019
ER -