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Intrusion Detection through Behavioral Data

  • Daniele Gunetti
  • Giancarlo Ruffo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1642)

Abstract

We present an approach to the problem of detecting intrusions in computer systems through the use behavioral data produced by users during their normal login sessions. In fact, attacks may be detected by observing abnormal behavior, and the technique we use consists in associating to each system user a classifier made with relational decision trees that will label login sessions as “legals” or as “intrusions”. We perform an experimentation for 10 users, based on their normal work, gathered during a period of three months.We obtain a correct user recognition of 90%, using an independent test set. The test set consists of new, previously unseen sessions for the users considered during training, as well as sessions from users not available during the training phase. The obtained performance is comparable with previous studies, but (1) we do not use information that may effect user privacy and (2) we do not bother the users with questions.

Keywords

Decision Tree Intrusion Detection Intrusion Detection System Inductive Logic Programming Legal User 
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 1999

Authors and Affiliations

  • Daniele Gunetti
    • 1
  • Giancarlo Ruffo
    • 1
  1. 1.Dept. of Computer ScienceUniversity of TorinoTorinoItaly

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