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A geometric approach to clustering based anomaly detection for industrial applications

 
: Li, Peng; Niggemann, Oliver; Hammer, Barbara

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 : Washington D.C., USA, October 21-23, 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5090-6684-1
S.5345-5352
IEEE Industrial Electronics Society (IECON Annual Conference) <44, 2018, Washington/DC>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
anomaly detection; cyber-physical production systems; non-convex hull

Abstract
Recent clustering based anomaly detection technologies classify new observations in different ways, e.g. using probability distributions, cluster centers or whole data points. Some of which suffer from high false classification rate, while others require high computational resources. In this paper, we propose a geometric approach to clustering based anomaly detection, in which the boundaries of clusters are utilized to classify new observations instead. To identify the cluster boundaries, a new algorithm for generating n-dimensional non-convex hulls has been developed. The proposed approach can improve the accuracy of clustering based anomaly detection, meanwhile, doesn't need high computational resources. Furthermore, it is universally applicable for any kind of cluster algorithms. The effectiveness of this approach is evaluated with real world data collected from different industrial automation systems.

: http://publica.fraunhofer.de/dokumente/N-537496.html