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Related Experiment Video

Updated: May 5, 2026

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
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Traffic Characterization of Event-Triggered Multiagent Systems Under FDI Attacks.

Tao Chen, Wentuo Fang, Wenfeng Hu

    IEEE Transactions on Cybernetics
    |March 3, 2026
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    Summary

    This study analyzes how false data injection attacks impact periodic event-triggered multiagent systems (MASs). We developed a traffic model to detect anomalous triggering behaviors and identify attack effects on minimum interevent time (MIET).

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    Area of Science:

    • Control Systems Engineering
    • Cybersecurity
    • Networked Systems

    Background:

    • Event-triggered systems offer efficiency but are vulnerable to cyberattacks.
    • False data injection (FDI) attacks pose a significant threat to the stability and reliability of multiagent systems (MASs).

    Purpose of the Study:

    • To investigate the impact of multiplicative FDI attacks on the triggering behaviors of periodic event-triggered MASs.
    • To develop a traffic model for characterizing triggering behaviors and analyzing attack effects on minimum interevent time (MIET).
    • To propose a method for selecting sampling periods for anomaly detection under FDI attacks.

    Main Methods:

    • Developed an abstraction-based traffic model to characterize triggering behaviors, including MIET and interevent time (IET) transitions.
    • Analyzed the influence of FDI attacks on MIET.
    • Designed a behavior-based anomaly detection algorithm using the traffic model.

    Main Results:

    • The proposed traffic model effectively characterizes triggering behaviors under various initial states.
    • Quantified the impact of FDI attacks on MIET.
    • Demonstrated the effectiveness of the anomaly detection algorithm in identifying anomalous triggering behaviors caused by attacks.

    Conclusions:

    • The study provides a framework for understanding and mitigating FDI attacks in event-triggered MASs.
    • The developed anomaly detection method is effective for practical applications in securing MASs.