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Detection of Point Anomalies in Railway Intelligent Control System Using Fast Clustering Techniques

  • Andrey V. Chernov
  • Ilias K. Savvas
  • Maria A. Butakova
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

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.

Keywords

Network anomaly detection Point anomalies Clustering DBSCAN Intelligent transportation system 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Andrey V. Chernov
    • 1
  • Ilias K. Savvas
    • 2
  • Maria A. Butakova
    • 1
  1. 1.Department of Computers and Automated Control SystemsRostov State Transport UniversityRostov-on-DonRussia
  2. 2.Department of Computer Science and EngineeringT.E.I. of ThessalyLarissaGreece

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