Anomaly detection of computer usage using artificial intelligence techniques

Jongho Choy, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


Intrusion detection systems (IDS) aim to detect attacks against computer systems by monitoring the behavior of users, networks, or computer systems. Attacks against computer systems are still largely successful despite the plenty of intrusion prevention techniques available. This paper presents an IDS based on anomaly detection using several AI techniques. Anomaly detection models normal behaviors and attempts to detect intrusions by noting significant deviations from normal behavior. Raw audit data are preprocessed and reduced into appropriate size and format using Self-Organizing Map (SOM). Different aspects of a sequence of events are modeled by several hidden Markov models (HMMs), and a voting technique combines the models to determine whether current behavior is normal or not. Several experiments are conducted to explore the optimal data reduction and modeling method. For the optimal measures, system call and file access related measures are found useful and overall performance depends on the map size for each measure. Voting technique leads to more reliable detection rate.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence
Subtitle of host publicationPRICAI 2000 Workshop Reader - Four Workshops held at PRICAI 2000, Revised Papers
EditorsRyszard Kowalczyk, Seng W. Loke, Nancy E. Reed, Graham Williams
PublisherSpringer Verlag
Number of pages13
ISBN (Print)3540425977, 9783540454083
Publication statusPublished - 2001
Event6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000 - Melbourne, Australia
Duration: 2000 Aug 282000 Sept 1

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
ISSN (Print)0302-9743


Other6th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2000

Bibliographical note

Publisher Copyright:
© 2001 Springer-Verlag Berlin Heidelberg.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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