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Optimal Stealthy Attacks With Energy Constraint Against Remote State Estimation.

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    IEEE Transactions on Cybernetics
    |September 28, 2023
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    Summary
    This summary is machine-generated.

    This study introduces an energy-constrained stealthy attack strategy for cyber-physical systems. It optimizes attack signals and schedules to degrade remote state estimation performance effectively.

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    Area of Science:

    • Cyber-Physical Systems
    • Control Theory
    • Network Security

    Background:

    • Remote state estimation in cyber-physical systems is vulnerable to stealthy attacks.
    • Energy constraints on attackers complicate the design of effective attack strategies.
    • Existing methods often assume prescribed attack signals, limiting optimality.

    Purpose of the Study:

    • To develop an energy-constrained stealthy attack strategy for remote state estimation.
    • To collaboratively design attack signals and schedules for maximum estimation performance degradation.
    • To address the coupling issue between attack schedules and signals without sacrificing optimality.

    Main Methods:

    • A two-step approach is proposed: deriving optimal attack signals for a given schedule, then finding the optimal schedule.
    • A nonlinear 0-1 programming problem is solved using a search space reduction algorithm.
    • The method efficiently eliminates non-optimal solutions to find the best attack strategy.

    Main Results:

    • The proposed strategy effectively deteriorates remote state estimation performance under energy constraints.
    • The collaborative design framework successfully addresses the coupling between attack signals and schedules.
    • Simulation results validate the theoretical findings and the effectiveness of the proposed method.

    Conclusions:

    • The developed energy-constrained stealthy attack strategy is optimal and efficient.
    • This research provides a robust method for analyzing and designing sophisticated attacks in cyber-physical systems.
    • The findings contribute to enhancing the security and resilience of remote state estimation systems.