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Generating Artificial Sensor Data for the Comparison of Unsupervised Machine Learning Methods.

Bernd Zimmering1, Oliver Niggemann1, Constanze Hasterok2

  • 1Institute of Automation Technology, Helmut-Schmidt-University, 22043 Hamburg, Germany.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
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This study introduces a novel data generation model for Cyber-Physical Systems (CPS) to enable fair machine learning algorithm comparisons. The model facilitates standardized benchmarking for unsupervised learning in CPS anomaly detection.

Area of Science:

  • Cyber-Physical Systems (CPS)
  • Machine Learning
  • Data Science

Background:

  • The proliferation of machine learning methods in Cyber-Physical Systems (CPS) is hindered by a lack of standardized datasets.
  • Developers currently create bespoke benchmarks, preventing fair comparisons and objective evaluation of new algorithms.
  • This situation impedes progress in developing robust and reliable CPS applications.

Purpose of the Study:

  • To propose a novel, system theory-based data generation model for creating synthetic datasets tailored to Cyber-Physical Systems.
  • To establish a foundation for fair and reproducible benchmarking of machine learning algorithms within the CPS domain.
  • To facilitate the comparative analysis of unsupervised learning techniques for anomaly detection in CPS.

Main Methods:

Keywords:
anomaly detectionartificial datamachine learning

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  • Development of a novel data generation model grounded in established system theory principles.
  • Generation of diverse synthetic datasets with varying complexity characteristics representative of CPS environments.
  • Evaluation of the data generation process through the performance analysis of selected unsupervised learning algorithms: Self-Organizing Map, One-Class Support Vector Machine, and Long Short-Term Memory Neural Network.

Main Results:

  • The proposed model successfully generates synthetic CPS data suitable for benchmarking machine learning algorithms.
  • The generated data enables a comparative analysis of unsupervised learning methods for anomaly detection.
  • Performance evaluation provides insights into the effectiveness of different algorithms on synthetic CPS data.

Conclusions:

  • The novel data generation model addresses the critical need for standardized datasets in CPS research.
  • This approach facilitates objective comparisons of machine learning algorithms, particularly in unsupervised anomaly detection.
  • The generated synthetic data serves as a valuable resource for advancing research and development in Cyber-Physical Systems.