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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Nonlinear Dynamics

    Background:

    • Observer-based sliding mode control (SMC) is crucial for fuzzy nonlinear systems.
    • Multimode denial of service (DoS) and deception attacks pose significant threats to system stability.
    • Existing attack models often fail to capture the dynamic and stochastic nature of complex cyberattacks.

    Purpose of the Study:

    • To develop a novel multimode DoS attack model with time-varying sojourn probabilities.
    • To model deception attacks using unbounded nonlinear functions and adaptive neural networks (NNs).
    • To design a robust observer-based SMC strategy for fuzzy nonlinear systems under cyberattacks.

    Main Methods:

    • Introduced a novel multimode DoS attack model incorporating time-varying sojourn probabilities.
    • Modeled deception attacks as unbounded nonlinear functions approximated by adaptive NNs.
    • Designed a fuzzy sliding surface and an observer-based SMC law ensuring mean square estimation upper bound.

    Main Results:

    • The proposed DoS model accurately represents stochastic attack behaviors, overcoming Markov-based model limitations.
    • Adaptive NNs effectively mitigated the impact of deception attacks on system stability.
    • The developed observer-based SMC law guaranteed the mean square estimation upper bound for fuzzy nonlinear systems.

    Conclusions:

    • The proposed control strategy effectively ensures the stability of fuzzy nonlinear systems against multimode DoS and deception attacks.
    • The novel attack modeling and adaptive NN approach offer a significant advancement in robust control under cyber threats.
    • Validation through a tunnel diode circuit model confirms the strategy's superiority and effectiveness.