Anomaly intrusion detection based on clustering a data stream

Sang Hyun Oh, Jin Suk Kang, Yung Cheol Byun, Taikyeong T. Jeong, Won Suk Lee

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

1 Citation (Scopus)


In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set. This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the ongoing result of clustering.

Original languageEnglish
Title of host publicationInformation Security - 9th International Conference, ISC 2006, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)3540383417, 9783540383413
Publication statusPublished - 2006
Event9th International Information Security Conference, ISC 2006 - Samos Island, Greece
Duration: 2006 Aug 302006 Sept 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4176 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th International Information Security Conference, ISC 2006
CitySamos Island

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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