Abstract
A novel attack detection method is presented for a nonlinear system with known dynamics using the measured output in the presence of additive process and measurement noise. False data injection (FDI) and replay attacks are considered using a modified fault detector. The difference between the measured and the estimated output from an adaptive observer, often known as the innovation signal, is generated and shown to have a Gaussian distribution with non-zero mean. This innovation signal in conjunction with the modified detector is utilized to detect attacks under a stable controller using the estimated state vector. Unlike FDI attack, where the output estimation signal changes its distribution, replay attack cannot be detected using this detector. Therefore, a modified watermarking approach using injected authentication noise to the estimator is introduced to detect such sophisticated attacks, and this approach is shown not to cause a deterioration in the system performance. Upon detecting the attacks, the observer dynamics are modified using a neural network to estimate the effective attack signal on the system dynamics, and in turn, to mitigate for it to keep the system performance undisturbed.
Recommended Citation
C. Bhowmick and S. Jagannathan, "Detection and Mitigation of Attacks in Nonlinear Stochastic System using Modified Detector," Proceedings of the IEEE Conference on Decision and Control, pp. 139 - 144, article no. 9029553, Institute of Electrical and Electronics Engineers, Dec 2019.
The definitive version is available at https://doi.org/10.1109/CDC40024.2019.9029553
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-172811398-2
International Standard Serial Number (ISSN)
2576-2370; 0743-1546
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Dec 2019
Comments
National Science Foundation, Grant CMMI 1547042