This paper focuses on the issue of anomaly detection for data in process control systems (PCSs). Considering data features in PCSs, this paper proposes to utilize the notion of one-class classification (OCC). In order to provide a general solution for more types of systems, ensemble learning is combined with OCC models. Two different ensembles of OCC models are proposed based on different scenarios in the process of detection. Performance of the proposed detection scheme is validated via several UCI datasets and two practical PCSs.
Diversity Aware-Based Sequential Ensemble Learning for Robust Anomaly Detection
