Neural Network-Based Attack Detection in Nonlinear Networked Control Systems
The communication links in networked control systems are vulnerable to various malicious attacks. In this paper, we propose a novel network attack detection scheme that is able to capture the abnormality in the traffic flow caused by a class of attacks targeting at the communication links. We model the network traffic flow in the bottleneck as a nonlinear system with unknown dynamics. By utilizing an observer, network attack detection residual is generated which is used to determine the existence of attacks in the networks when the residual exceeds a predefined threshold. We also revisit an optimal event-triggered controller for the physical system and derive the maximum delay and packet loss that the system can tolerate.
H. Niu and J. Sarangapani, "Neural Network-Based Attack Detection in Nonlinear Networked Control Systems," Proceedings of the 2016 International Joint Conference on Neural Networks (2016, Vancouver, Canada), pp. 4249-4254, Institute of Electrical and Electronics Engineers (IEEE), Jul 2016.
The definitive version is available at https://doi.org/10.1109/IJCNN.2016.7727754
2016 International Joint Conference on Neural Networks, IJCNN (2016: Jul. 24-29, Vancouver, Canada)
Electrical and Computer Engineering
Keywords and Phrases
Computer crime; Control systems; Neural networks; Cyber-attacks; Event-triggered; Malicious attack; Network attack; Network traffic flow; Network-based attacks; Nonlinear networked control systems; Physical systems; Networked control systems; Cyber-attack detection; Network traffic flow control
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.