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Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security.

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Summary

This study introduces Bayesian inference for detecting anomalies in cyber-physical systems, even with limited data. The method effectively quantifies uncertainty for enhanced system security.

Keywords:
Bayesian estimationcyber-physical securitydynamical systemssensors and actuators

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Area of Science:

  • Cyber-physical Systems
  • Measurement Science and Technology
  • Statistical Inference

Background:

  • Cyber-physical systems (CPS) security faces challenges due to limited parameter spaces in anomaly detection.
  • Software-assisted physical systems, like those in additive manufacturing or DNA synthesis, often present ill-posed problems for anomaly detection.
  • Conventional methods struggle with the inherent complexities of CPS anomaly detection.

Purpose of the Study:

  • To present a novel Bayesian inference method for detecting anomalies in cyber-physical systems.
  • To address the challenge of limited data and parameter spaces in CPS anomaly detection.
  • To quantify the uncertainty associated with anomaly detection in CPS.

Main Methods:

  • Utilized Bayesian inference to estimate unknown parameters of a generic cyber-physical system actuator.
  • Obtained a numerical transfer function model from experimental input-output measurements.
  • Simulated a code-based malicious signal to test the detection efficacy of the Bayesian approach.

Main Results:

  • Demonstrated the efficacy of Bayesian inference for anomaly detection using a minimal number of data points.
  • Achieved effective anomaly detection with uncertainty quantification.
  • The developed model successfully identified malicious signals within the cyber-physical system.

Conclusions:

  • Bayesian inference offers a robust solution for real-time anomaly detection in cyber-physical systems.
  • The proposed method is adaptable to various CPS anomaly detection scenarios.
  • This approach enhances the security of cyber-physical systems by providing reliable detection with uncertainty quantification.