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    Area of Science:

    • Cyber-Physical Systems Security
    • Networked Control Systems
    • Data-Driven Attack Methodologies

    Background:

    • Existing stealthy attacks on CPS often assume attackers know system parameters or can modify sensor data.
    • Model-independent attacks typically require knowledge and manipulation of sensor measurements, limiting their applicability.

    Purpose of the Study:

    • To develop a stealthy attack methodology for closed-loop CPS, specifically Transmission Control Protocol/Internet Protocol (TCP/IP)-based networked control systems.
    • To remove the conservative assumptions of known model parameters and sensor data manipulation in stealthy attack research.

    Main Methods:

    • Constructed a benchmark platform using data-driven methods for testing stealthy attacks on a TCP/IP-based networked dc servo system.
    • Developed a strategy for eavesdropping TCP/IP-based CPS information.
    • Explored methods for blocking network communications and injecting false sensor data.
    • Designed a closed-loop recursive identification strategy for the CPS dynamic characteristic matrix.

    Main Results:

    • Proposed a data-driven stealthy attack scheme for CPS under unknown model parameters.
    • Successfully validated the effectiveness and practicability of the proposed attack scheme through experimentation.
    • Demonstrated a method for network communication blocking and false sensor data injection.

    Conclusions:

    • The developed data-driven stealthy attack is effective and practical for closed-loop TCP/IP-based CPS.
    • The methodology successfully bypasses the need for attackers to know CPS model parameters or sensor data.
    • This research advances the understanding and defense against sophisticated cyber threats in networked control systems.