Non-convex hull based anomaly detection in CPPS

Along with the constantly increasing complexity of industrial automation systems, machine learning methods have been widely applied to detecting abnormal states in such systems. Anomaly detection tasks can be treated as one-class classification problems in machine learning. Geometric methods can give an intuitive solution to such problems. In this paper, we propose a new geometric structure, oriented non-convex hulls, to represent decision boundaries used for one-class classification. Based on this geometric structure, a novel boundary based one-class classification algorithm is developed to solve the anomaly detection problem. Compared with traditional boundary-based approaches such as convex hulls based methods and one-class support vector machines, the proposed approach can better reflect the true geometry of target data and needs little effort for parameter tuning. The effectiveness of this approach is evaluated with artificial and real world data sets to solve the anomaly detection problem in Cyber-Physical-Production-Systems (CPPS). The evaluation results also show that the proposed approach has higher generality than the used baseline algorithms.