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A Toolset for Intrusion and Insider Threat Detection

  • Markus RingEmail author
  • Sarah Wunderlich
  • Dominik Grüdl
  • Dieter Landes
  • Andreas Hotho
Chapter
Part of the Data Analytics book series (DAANA)

Abstract

Company data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.

Notes

Acknowledgements

This work is funded by the Bavarian Ministry for Economic affairs through the WISENT project (grant no. IUK 452/002).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Markus Ring
    • 1
    Email author
  • Sarah Wunderlich
    • 1
  • Dominik Grüdl
    • 1
  • Dieter Landes
    • 1
  • Andreas Hotho
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceCoburg University of Applied Sciences and ArtsCoburgGermany
  2. 2.Data Mining and Information Retrieval GroupUniversity of WürzburgWürzburgGermany

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