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

Haifeng Niu, Chandreyee Bhowmick, Sarangapani Jagannathan

    IEEE Transactions on Neural Networks and Learning Systems
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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a neural network (NN) approach to detect and estimate attacks in networked control systems (NCS). The method identifies abnormal network traffic and secures physical systems against network and sensor attacks.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Networked Control Systems (NCS) are vulnerable to communication network attacks causing traffic anomalies like delays and packet losses.
    • Existing methods may not adequately address complex attack scenarios in NCS.
    • Securing NCS against sophisticated network and sensor attacks is critical for reliable operation.

    Purpose of the Study:

    • To develop a novel neural network (NN)-based scheme for detecting and estimating attacks in NCS.
    • To model unknown network flow as a nonlinear function and utilize a NN observer for attack detection.
    • To implement an adaptive dynamic programming-based optimal event-triggered NN controller for physical system security.

    Main Methods:

    • Utilizing a NN observer to model network flow and generate an attack detection residual.
    • Defining a threshold for the residual to determine the onset of network attacks.
    • Employing a second NN for estimating the magnitude of the injected attack flow.
    • Developing an adaptive dynamic programming-based optimal event-triggered NN controller for the physical system.

    Main Results:

    • The proposed NN-based scheme effectively detects and estimates attacks targeting the communication network in NCS.
    • The method successfully identifies abnormal traffic flows indicative of specific attack classes.
    • The adaptive NN controller enhances the security of the physical system against network and sensor attacks.
    • Simulation results validate the theoretical findings and the efficacy of the proposed scheme.

    Conclusions:

    • The NN-based attack detection and estimation scheme provides a robust solution for securing NCS.
    • The integrated approach addresses both network-level and sensor-level attacks within NCS.
    • The developed methodology offers enhanced resilience and security for critical infrastructure relying on NCS.