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Optimal Innovation-Based Stealthy Attacks in Networked LQG Systems With Attack Cost.

Kaijing Jin, Dan Ye

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    |April 4, 2023
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    Summary

    Researchers developed optimal stealthy attack strategies for networked linear quadratic Gaussian (LQG) systems. These novel methods enhance attack effectiveness while maintaining stealthiness, outperforming existing suboptimal approaches.

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Optimization Theory

    Background:

    • Networked systems are vulnerable to cyberattacks.
    • Linear Quadratic Gaussian (LQG) systems are widely used in control applications.
    • Stealthy attacks aim to evade detection while disrupting system performance.

    Purpose of the Study:

    • To investigate optimal innovation-based attack strategies for networked LQG systems.
    • To develop methods for achieving strict or epsilon-stealthiness using Kullback-Leibler divergence.
    • To maximize attacker objectives (control cost increase, attack cost decrease).

    Main Methods:

    • Formulating the attack strategy as a nonconvex optimization problem.
    • Transforming the objective function into a linear form using matrix trace properties.
    • Employing matrix decomposition for strictly stealthy attacks.
    • Utilizing integrated convex optimization for epsilon-stealthy attacks.

    Main Results:

    • An optimal strictly stealthy attack strategy was derived.
    • An optimal epsilon-stealthy attack strategy was developed, offering higher attack effects.
    • The proposed integrated convex optimization approach surpasses existing two-stage suboptimal methods.
    • Simulation results validated the effectiveness of the designed attack strategies.

    Conclusions:

    • The study presents effective optimal stealthy attack strategies for networked LQG systems.
    • The developed methods provide a significant advancement over existing suboptimal attack techniques.
    • These findings have implications for understanding and defending against sophisticated cyber threats in control systems.