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Secure Distributed Finite-Time Filtering for Positive Systems Over Sensor Networks Under Deception Attacks.

Shunyuan Xiao, Qing-Long Han, Xiaohua Ge

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    This study develops secure distributed filters for positive discrete-time systems facing deception attacks in sensor networks. The filters ensure finite-time performance, enhancing system security and reliability.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems Security
    • Positive System Theory

    Background:

    • Sensor networks are crucial for data acquisition but vulnerable to deception attacks.
    • Ensuring secure and reliable data processing in positive discrete-time systems is challenging.
    • Network-induced constraints like communication link failures impact system performance.

    Purpose of the Study:

    • To analyze the secure l1-gain performance of positive discrete-time linear systems.
    • To design distributed finite-time filters robust to deception attacks.
    • To address challenges posed by network topology and communication failures.

    Main Methods:

    • Development of a unified sensor measurement transmission model incorporating deception attacks and network constraints.
    • Design of secure distributed filters that accommodate corrupted sensor measurements.
    • Finite-time l1-gain boundedness analysis for the filtering error system.

    Main Results:

    • Characterization of filter gain parameters using linear programming inequalities.
    • Demonstration of finite-time secure performance for the filtering error system.
    • Successful verification through secure monitoring of smart grid power distribution.

    Conclusions:

    • The proposed filters effectively handle deception attacks and network constraints in positive discrete-time systems.
    • Finite-time l1-gain analysis provides a robust framework for filter design.
    • The methodology is validated for practical applications like smart grid security.