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Summary

This study introduces application-aware anomaly detection (AAAD) to minimize the impact of false alarms and missed faults in anomaly detection systems. The AAAD approach optimizes detector configuration for improved application performance despite inevitable detection errors.

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anomaly detectioncyber-physical systemsdetection errortraffic sensors

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Systems Engineering

Background:

  • Anomaly detection systems are prone to inevitable errors like false alarms and missed detections.
  • These errors can negatively impact application performance, leading to unnecessary actions or critical failures.

Purpose of the Study:

  • To present an application-aware anomaly detection (AAAD) approach.
  • To configure existing anomaly detectors to minimize the impact of detection errors on application performance.

Main Methods:

  • Developed an AAAD approach that configures anomaly detectors.
  • Optimized detector configuration to maintain application performance close to ideal levels, even with errors.
  • Evaluated the approach using a real-time traffic signal control case study.

Main Results:

  • The AAAD approach significantly outperformed several baseline anomaly detection methods.
  • Demonstrated improved application performance in the presence of detection errors.

Conclusions:

  • Application-aware anomaly detection is effective in mitigating the negative consequences of detection errors.
  • The proposed AAAD method offers a significant improvement over traditional anomaly detection techniques, particularly in critical systems.