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Optimal Strictly Stealthy Attack Design on Cyber-Physical Systems: A Data-Driven Approach.

Zhuyuan Li, Zhengen Zhao, Steven X Ding

    IEEE Transactions on Cybernetics
    |July 10, 2024
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    Summary

    This study introduces a data-driven method for designing stealthy attacks on stochastic systems without prior system knowledge. The approach maximizes performance degradation while evading detection, offering insights into cyber-physical system vulnerabilities.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Systems Theory

    Background:

    • Cyber-physical systems are vulnerable to sophisticated attacks.
    • Stealthy attacks aim to degrade system performance while evading detection.
    • Existing methods often require attackers to possess full system knowledge.

    Purpose of the Study:

    • To develop a data-driven optimal strictly stealthy attack design for stochastic linear invariant systems.
    • To maximize system performance degradation under energy constraints.
    • To bypass parity-space-based attack detectors without assuming system knowledge.

    Main Methods:

    • A novel strictly stealthy attack sequence (SSAS) is proposed, coordinating sensor and actuator signals.
    • The SSAS is parameterized in the null space of a matrix derived from parity and Markov parameters.
    • Modified subspace identification methods are used for unbiased parameter estimation from closed-loop data.

    Main Results:

    • A sufficient and necessary condition for the existence of the SSAS is presented.
    • The attack design is formulated as a constrained optimization problem with an explicit solution.
    • The vulnerability analysis provides insights into detector parameter selection.

    Conclusions:

    • The proposed data-driven approach enables optimal stealthy attack design without system knowledge.
    • The method effectively maximizes performance degradation while evading detection.
    • The study highlights vulnerabilities in cyber-physical systems and informs detector design.