Attack Detection and Approximation in Nonlinear Networked Control Systems Using Neural Networks

In networked control systems (NCS), a certain class of attacks on the communication network is known to raise traffic flows causing delays and packet losses to increase. This paper presents a novel neural network (NN)-based attack detection and estimation scheme that captures the abnormal traffic fl...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 31; H. 1; S. 235 - 245
Hauptverfasser: Niu, Haifeng, Bhowmick, Chandreyee, Jagannathan, Sarangapani
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Actor-critic network
Adaptive systems
Artificial neural networks
attack detection
attack estimation
Communication
Communication networks
Communications networks
Computer simulation
Control systems
Control theory
Delays
Dynamic programming
event-triggered control
Feedback loops
flow control
networked control system (NCS)
neural network (NN)
Neural networks
Nonlinear control
Nonlinear systems
Observers
optimal control
Sensors
Traffic congestion
Traffic delay
Traffic flow
ISSN:2162-237X, 2162-2388, 2162-2388
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In networked control systems (NCS), a certain class of attacks on the communication network is known to raise traffic flows causing delays and packet losses to increase. This paper presents a novel neural network (NN)-based attack detection and estimation scheme that captures the abnormal traffic flow due to a class of attacks on the communication links within the feedback loop of an NCS. By modeling the unknown network flow as a nonlinear function at the bottleneck node and using a NN observer, the network attack detection residual is defined and utilized to determine the onset of an attack in the communication network when the residual exceeds a predefined threshold. Upon detection, another NN is used to estimate the flow injected by the attack. For the physical system, we develop an attack detection scheme by using an adaptive dynamic programming-based optimal event-triggered NN controller in the presence of network delays and packet losses. Attacks on the network as well as on the sensors of the physical system can be detected and estimated with the proposed scheme. The simulation results confirm theoretical conclusions.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2900430