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Set-Membership Estimation for Complex Networks Subject to Linear and Nonlinear Bounded Attacks.

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    IEEE Transactions on Neural Networks and Learning Systems
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    This study introduces a new set-membership estimation model to handle unknown bounded attacks in complex networks. It ensures reliable estimation even when communication channels are compromised by adversaries.

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    Area of Science:

    • Control Systems Engineering
    • Network Security
    • Estimation Theory

    Background:

    • Complex networks are vulnerable to unknown but bounded attacks in communication channels.
    • Adversarial modifications to transmitted measurements pose significant challenges for accurate estimation.
    • Existing estimation methods may not adequately address these sophisticated attack scenarios.

    Purpose of the Study:

    • To develop a novel set-membership estimation model resilient to unknown but bounded attacks.
    • To establish conditions guaranteeing the existence of such estimators for linear and nonlinear attacks.
    • To propose strategies for designing effective set-membership estimator gains.

    Main Methods:

    • Formulation of a new set-membership estimation framework.
    • Derivation of sufficient conditions for estimator existence under linear and nonlinear attack functions.
    • Development of two distinct strategies for designing estimator gains.
    • Validation through simulation examples.

    Main Results:

    • A novel set-membership estimation model against unknown but bounded attacks is presented.
    • Sufficient conditions are derived to ensure the existence of the estimators.
    • Two effective strategies for designing the set-membership estimator gains are proposed.
    • Simulation results confirm the method's effectiveness.

    Conclusions:

    • The proposed set-membership estimation approach effectively addresses unknown bounded attacks in complex networks.
    • The derived conditions and design strategies provide a robust framework for secure estimation.
    • The validated method enhances the reliability of complex network systems under adversarial conditions.