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  3. Distributed Consensus Estimation For Networked Multi-sensor Systems Under Hybrid Attacks And Missing Measurements.
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  3. Distributed Consensus Estimation For Networked Multi-sensor Systems Under Hybrid Attacks And Missing Measurements.

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Distributed Consensus Estimation for Networked Multi-Sensor Systems under Hybrid Attacks and Missing Measurements.

Zhijian Cheng1, Lan Yang1, Qunyao Yuan1

  • 1School of Automation, Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, and Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control, Guangdong University of Technology, Guangzhou 510006, China.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study addresses cyber-attacks on networked multi-sensor systems by developing a distributed consensus estimator resilient to hybrid attacks and missing data. The research enhances system security and reliability in critical applications.

Keywords:
distributed consensus estimationhybrid attacksmissing measurementsnetworked multi-sensor systems

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Area of Science:

  • Networked systems security
  • Distributed estimation theory
  • Cyber-physical systems

Background:

  • Networked multi-sensor systems are vulnerable to sophisticated cyberattacks, including denial of service (DoS) and false data injection (FDI).
  • Effective defense strategies require understanding attack behaviors from an attacker's viewpoint.
  • Missing measurements further complicate reliable distributed estimation.

Purpose of the Study:

  • To develop a robust distributed consensus estimation algorithm for networked multi-sensor systems facing hybrid attacks and missing measurements.
  • To model hybrid attacks (DoS and FDI) on communication channels between estimators.
  • To design a scalable, suboptimal estimator that reduces computational complexity while ensuring stability.

Main Methods:

  • A hybrid attack model incorporating DoS and FDI attacks on the estimator-to-estimator communication channel was developed.
  • Missing measurements were modeled using Bernoulli-distributed random variables.
  • A modified consensus-based distributed estimator was designed to integrate hybrid attack and missing measurement characteristics.
  • A scalable suboptimal distributed consensus estimator was proposed to mitigate computational load.
  • Sufficient conditions for the stability of the suboptimal estimator were derived.

    • Main Results:

    • The proposed modified distributed estimator effectively accounts for hybrid attacks and missing measurements.
    • A scalable suboptimal estimator was designed, offering reduced computational complexity compared to optimal methods.
    • Sufficient conditions guaranteeing the stability of the suboptimal estimator were established.
    • Simulation experiments on aircraft tracking demonstrated the algorithm's effectiveness and feasibility.

    Conclusions:

    • The developed distributed consensus estimator provides a robust solution for networked multi-sensor systems under hybrid cyberattacks and data loss.
    • The suboptimal design offers a practical approach for real-time applications with reduced computational demands.
    • The stability analysis ensures reliable performance in challenging cyber-physical environments.