Abstract
A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems NCS, wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.
Recommended Citation
H. Niu et al., "An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems," IEEE/CAA Journal of Automatica Sinica, vol. 6, no. 6, pp. 1404 - 1416, article no. 8894751, Institute of Electrical and Electronics Engineers, Nov 2019.
The definitive version is available at https://doi.org/10.1109/JAS.2019.1911762
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Serial Number (ISSN)
2329-9274; 2329-9266
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Nov 2019
Comments
National Science Foundation, Grant CMMI 1547042