Information Security

Volume 4176 of the series Lecture Notes in Computer Science pp 415-426

Anomaly Intrusion Detection Based on Clustering a Data Stream

  • Sang-Hyun OhAffiliated withCarnegie Mellon UniversityDept. of Computer Science, Yonsei Univ.
  • , Jin-Suk KangAffiliated withCarnegie Mellon UniversityDept. of Computer Eng., Kunsan National Univ.
  • , Yung-Cheol ByunAffiliated withCarnegie Mellon UniversityDept. of Communication & Computer Eng, Cheju National Univ.
  • , Taikyeong T. JeongAffiliated withCarnegie Mellon UniversityDept. of Electrical & Computer Engineering, University of Texas at Austin
  • , Won-Suk LeeAffiliated withCarnegie Mellon UniversityDept. of Computer Science, Yonsei Univ.

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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 on-going result of clustering.


Intrusion detection Anomaly detection Data mining Clustering Data stream