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Shifting Attack Stabilization and Estimation of Hidden Markov Boolean Networks.

Liqing Wang, Zheng-Guang Wu

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    This study introduces a shifting attack for hidden Markov Boolean control networks (HMBCNs). It presents methods for stochastic stabilization and security analysis, ensuring network control system resilience against sophisticated cyber threats.

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    Area of Science:

    • Control Theory
    • Network Security
    • Cyber-Physical Systems

    Background:

    • Hidden Markov Boolean Control Networks (HMBCNs) are complex systems susceptible to sophisticated network attacks.
    • Understanding and mitigating shifting attacks is crucial for maintaining the stability and security of these networks.

    Purpose of the Study:

    • To analyze and develop control strategies for HMBCNs under a novel 'shifting attack'.
    • To establish conditions for stochastic stabilization and network security against such attacks.
    • To provide methods for estimating state and signal expectations in attacked HMBCNs.

    Main Methods:

    • Algebraic representation of HMBCNs and shifting attacks using the semi-tensor product of matrices.
    • Application of State Feedback Control (SFC) for network stabilization.
    • Modeling shifting attacks as a hidden Markov process.
    • Utilizing the change of probability measurement approach for state estimation.

    Main Results:

    • A necessary and sufficient condition for the stochastic stabilization of HMBCNs was derived.
    • Conditions for the security and insecurity of HMBCNs under shifting attacks were established.
    • Effective methods for estimating the expectation of states and attacked signals were presented.

    Conclusions:

    • The proposed methods effectively stabilize HMBCNs and analyze security under shifting attacks.
    • The findings offer valuable insights into securing complex control networks against advanced cyber threats.
    • Demonstrated effectiveness through illustrative examples, validating the theoretical results.