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Intrusion Detection Based on Behavior Mining and Machine Learning Techniques

  • Srinivas Mukkamala
  • Dennis Xu
  • Andrew H. Sung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)

Abstract

This paper describes results concerning the classification capability of unsupervised and supervised machine learning techniques in detecting intrusions using network audit trails. In this paper we investigate well known machine learning techniques: Frequent Pattern Tree mining (FP-tree), classification and regression tress (CART), multivariate regression splines (MARS) and TreeNet. The best model is chosen based on the classification accuracy (ROC curve analysis). The results show that high classification accuracies can be achieved in a fraction of the time required by well known support vector machines and artificial neural networks. TreeNet performs the best for normal, probe and denial of service attacks (DoS). CART performs the best for user to super user (U2su) and remote to local (R2L).

Keywords

Support Vector Machine Intrusion Detection Frequent Pattern Terminal Node Intrusion Detection System 
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 2006

Authors and Affiliations

  • Srinivas Mukkamala
    • 1
  • Dennis Xu
    • 1
  • Andrew H. Sung
    • 1
  1. 1.Institute for Complex Additive Systems and AnalysisDepartment of Computer Science, New Mexico TechSocorro

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