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Using Unsupervised Learning for Network Alert Correlation

  • Reuben Smith
  • Nathalie Japkowicz
  • Maxwell Dondo
  • Peter Mason
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5032)

Abstract

Alert correlation systems are post-processing modules that enable intrusion analysts to find important alerts and filter false positives efficiently from the output of Intrusion Detection Systems. Typically, however, these modules require high levels of human involvement in creating the system and/or maintaining it, as patterns of attacks change as often as from month to month. We present an alert correlation system based on unsupervised machine learning algorithms that is accurate and low maintenance. The system is implemented in two stages of correlation. At the first stage, alerts are grouped together such that each group forms one step of an attack. At the second stage, the groups created at the first stage are combined such that each combination of groups contains the alerts of precisely one full attack. We tested various implementations of the system. The most successful one relies in the first stage on a new unsupervised algorithm inspired by an existing novelty detection system, and the EM algorithm in the second stage. Our experimental results show that, with our model, the number of alerts that an analyst has to deal with is significantly reduced.

Keywords

Cluster Algorithm Intrusion Detection Intrusion Detection System Hide Unit Attack Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Reuben Smith
    • 1
  • Nathalie Japkowicz
    • 1
  • Maxwell Dondo
    • 2
  • Peter Mason
    • 2
  1. 1.School of Information Technology and Engineering (SITE)University of Ottawa ON Canada 
  2. 2.Defence Research and Development Canada (DRDC) Ottawa ON Canada 

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