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Fusion and detection for multi-sensor systems under false data injection attacks.

Jinxing Hua1, Fei Hao1

  • 1The Seventh Research Division, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.

ISA Transactions
|June 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to detect false data injection attacks in cyber-physical systems. The method enhances security for multisensory frameworks, improving data reliability.

Keywords:
Cyber–physical systems (CPSs)DetectionExpected SARSAMulti-sensorPartially observable Markov decision process (POMDP)

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Area of Science:

  • Cyber-Physical Systems Security
  • Sensor Fusion
  • Wireless Communication Security

Background:

  • Cyber-physical systems (CPS) rely on sensor data for operation.
  • Wireless transmission in CPS is vulnerable to false data injection attacks.
  • Existing security measures may not adequately address sophisticated attacks.

Purpose of the Study:

  • To investigate and mitigate security vulnerabilities in multisensory CPS.
  • To develop a robust algorithm for detecting false data injection.
  • To design an optimal attack strategy for performance evaluation.

Main Methods:

  • A revised multi-sensor Kalman filter fusion algorithm is proposed.
  • An optimal linear false data injection attack strategy is designed.
  • An Expected SARSA-based algorithm is developed for attack detection.

Main Results:

  • The proposed algorithm effectively detects false data injection.
  • The optimal attack strategy is more general, accommodating arbitrary Gaussian distributions.
  • Simulations on an unmanned aerial vehicle demonstrate the algorithm's feasibility and efficiency.

Conclusions:

  • The developed algorithms enhance the security of multisensory CPS against false data injection.
  • The Expected SARSA approach improves attack detection performance.
  • The findings are validated through practical simulations.