Discretization of CPS Time Series with Neural Networks

This paper investigates the application of machine learning techniques for discretizing multivariate time series data in cyber-physical systems, emphasizing an unsupervised learning approach. Due to the lack of system information about the systems under investigation, we focus our work on the conversion of high-dimensional, continuous data into discrete state representations to improve the interpretability of the system. The study evaluates several unsupervised machine learning methods using both simulated and real datasets of cyber-physical systems, particularly examining their utility in anomaly detection tasks. Our analysis highlights the trade-offs between the complexity and the purity of the learned state representations, and how this influences the performance in subsequent anomaly detection applications. The paper provides a comprehensive view of how different machine-learning-based discretization methods perform under various conditions and offers practical guidelines for selecting the appropriate method. Through this work, we aim to contribute to the broader understanding and implementation of effective unsupervised machine learning strategies for data analysis in cyber-physical systems.