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Towards a Model- and Learning-Based Framework for Security Anomaly Detection

  • Matthias Gander
  • Basel Katt
  • Michael Felderer
  • Ruth Breu
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7542)

Abstract

For critical areas, such as the health-care domain, it is common to formalize workflow, traffic-flow and access control via models. Typically security monitoring is used to firstly determine if the system corresponds to the specifications in these models and secondly to deal with threats, e.g. by detecting intrusions, via monitoring rules. The challenge of security monitoring stems mainly from two aspects. First, information in form of models needs to be integrated in the analysis part, e.g. rule creation, visualization, such that the plethora of monitored events are analyzed and represented in a meaningful manner. Second, new intrusion types are basically invisible to established monitoring techniques such as signature-based methods and supervised learning algorithms.

In this paper, we present a pluggable monitoring framework that focuses on the above two issues by linking event information and modelling specification to perform compliance detection and anomaly detection. As input the framework leverages models that define workflows, event information, as well as the underlying network infrastructure. Assuming that new intrusions manifest in anomalous behaviour which cannot be foreseen, we make use of a popular unsupervised machine-learning technique called clustering.

Keywords

Modelling Profiling Machine Learning IT-Security Runtime-Monitoring Anomaly Detection Clustering 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matthias Gander
    • 1
  • Basel Katt
    • 1
  • Michael Felderer
    • 1
  • Ruth Breu
    • 1
  1. 1.Institute of Computer ScienceUniversity of InnsbruckAustria

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