Comparative analysis of unsupervised anomaly detection techniques in industrial systems
TL;DR
A comparative evaluation of unsupervised anomaly detection techniques using the PyOD Python library, applied to three representative industrial scenarios: Intrusion Detection, Fault Detection, and Predictive Maintenance, offers valuable guidance for selecting robust anomaly detection models in industrial applications beyond cybersecurity.
Abstract
This study presents a comparative evaluation of unsupervised anomaly detection techniques using the PyOD Python library, applied to three representative industrial scenarios: Intrusion Detection, Fault Detection, and Predictive Maintenance. Three public datasets, each corresponding to one of these domains, are used to assess the generalizability and robustness of the methods. The methodology includes data normalization, feature selection, and class balancing to address the specific challenges of each dataset, such as class imbalance and heterogeneous feature spaces. Performance is assessed using metrics such as Precision, Recall, Specificity, F1-score, AUC-PR, AUC-ROC, Balanced Accuracy, and Execution Time (training and testing). The findings offer valuable guidance for selecting robust anomaly detection models in industrial applications beyond cybersecurity, including those related to reliability and maintenance. The code and results will be publicly available on GitHub https://github.com/hvelesaca/PyOD_OPC-UA to support future research.