Abstract
This work presents a new area of application for clustering techniques in industrial and transport applications. The main aim of the research is to propose the technique for detection of point anomalies in telecommunication traffic produced by network subsystems of railway intelligent control system. The central idea behind is to apply enhanced DBSCAN algorithms for finding the outliers in traffic which are associated with unintended erroneous events or deliberated attacks targeted to infrastructure malfunction. The traffic flows in a part of the railway intelligent control system has been described in detail. Point anomaly detection in IP-networks data using distributed DBSCAN has been proposed. Series of computation experiments for outlier detection in network traffic has been implemented. The experiments showed the applicability of distributed DBSCAN technique to the robust detection of point anomalies caused by various incidents in the network infrastructure of railway intelligent control system.
The work was financially supported by Russian Foundation for Basic Research (projects 16-01-00597-a, 16-07-00888-a, 17-07-00620-a, 18-01-00402-a).
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Chernov, A.V., Savvas, I.K., Butakova, M.A. (2019). Detection of Point Anomalies in Railway Intelligent Control System Using Fast Clustering Techniques. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-01821-4_28
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