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    Area of Science:

    • Cyber-Physical Systems
    • Control Theory
    • Information Security

    Background:

    • Remote state estimation is crucial for monitoring cyber-physical systems.
    • Stealthy attacks pose a significant threat by degrading estimation performance undetected.
    • Existing attack models have limitations in flexibility and computational efficiency.

    Purpose of the Study:

    • To design an optimal strictly stealthy attack against remote state estimation.
    • To propose a novel corrupted innovation-based attack model.
    • To enhance attack design flexibility and performance degradation.

    Main Methods:

    • Developed a novel corrupted innovation-based attack model by combining intercepted and side information.
    • Derived an analytical optimal attack strategy.
    • Utilized numerical examples for verification.

    Main Results:

    • The proposed attack model offers higher design flexibility compared to nominal innovation-based attacks.
    • Eliminates the need for an additional filter, saving computational resources.
    • Achieves greater performance degradation of remote state estimation.

    Conclusions:

    • The novel attack model is superior and effective for degrading remote state estimation in cyber-physical systems.
    • Offers a more computationally efficient and flexible approach to stealthy attacks.
    • Highlights vulnerabilities in cyber-physical system security.