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    This study introduces a neural network-based resilient event-triggered control protocol to ensure input-to-state stability in networked control systems facing denial-of-service and deception attacks.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Networked control systems (NCSs) are vulnerable to cyber attacks like Denial-of-Service (DoS) and Deception Attacks (DAs).
    • Resource constraints in NCSs necessitate efficient control protocols.
    • Ensuring system stability under adversarial conditions is critical for reliable operation.

    Purpose of the Study:

    • To develop a robust control strategy for NCSs against combined DoS and DA.
    • To enhance the resilience and stability of NCSs using intelligent control methods.
    • To address resource limitations while maintaining system security.

    Main Methods:

    • A neural network (NN)-based resilient event-triggered control protocol (RETCP) was designed.
    • NNs were utilized to neutralize and approximate malicious data from DAs.
    • A novel predictor was developed to compensate for signal loss during DoS attacks.

    Main Results:

    • The proposed NN-based RETCP effectively mitigates the impact of DoS and DA.
    • The developed predictor enhances NCS tolerance to complex cyber threats.
    • The intelligent control protocols achieve input-to-state stability (ISS) under combined attacks.

    Conclusions:

    • The NN-based controller ensures ISS for NCSs facing sophisticated cyber attacks.
    • The proposed RETCP offers a viable solution for securing NCSs in challenging environments.
    • Experimental validation on an uncrewed ground vehicle (UGV) confirms the protocol's efficacy.