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Feature-Based Normality Models for Anomaly Detection.

Hui Yie Teh1, Kevin I-Kai Wang1, Andreas W Kempa-Liehr2

  • 1Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1142, New Zealand.

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|August 14, 2025
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Summary
This summary is machine-generated.

This study introduces an anomaly detection framework for Internet of Things sensor data. It effectively learns sensor-specific normality models from limited data, improving anomaly detection accuracy.

Keywords:
anomaly detectionfeature engineeringnormality modelsensor data qualitytime series analytics

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Sensor Data Analysis

Background:

  • Detecting novel anomalies in sensor data is challenging due to sensor-specific characteristics and short calibration periods.
  • Low-cost sensors in Internet of Things (IoT) deployments often have lower data quality, necessitating improved anomaly detection methods.
  • Existing methods struggle with the heterogeneity of sensor data and the need for rapid learning from limited data.

Purpose of the Study:

  • To develop an anomaly detection framework capable of learning sensor-specific normality models from limited, anomaly-free data.
  • To address the challenge of detecting previously unseen anomalies in heterogeneous sensor data from IoT deployments.
  • To improve the accuracy and reliability of anomaly detection in resource-constrained IoT environments.

Main Methods:

  • A framework that learns individual sensor-specific normality models using unsupervised feature engineering.
  • Utilisation of a Local Outlier Factor (LOF) model trained on statistically significant features to identify anomalies.
  • Evaluation on real-world environmental monitoring datasets with extremely short calibration periods (e.g., 3 days or 10% of data).

Main Results:

  • The proposed framework achieved superior performance compared to four state-of-the-art anomaly detection approaches.
  • Improvements in F1-score ranged from 5.4% to 9.3%, and Matthews correlation coefficient improved by 4.0% to 7.6%.
  • Effective anomaly detection was demonstrated even with very limited calibration data, highlighting the framework's efficiency.

Conclusions:

  • The developed framework successfully learns sensor-specific normality models, enabling robust anomaly detection in IoT settings.
  • The approach is effective in handling heterogeneous sensor data and short calibration periods, outperforming existing methods.
  • This work contributes a valuable tool for enhancing the reliability of data from low-cost sensors in IoT applications.