@inproceedings{8bb6cd4f73b0472cbc53aac4d626816b,
title = "Anomaly intrusion detection based on clustering a data stream",
abstract = "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.",
author = "Oh, {Sang Hyun} and Kang, {Jin Suk} and Byun, {Yung Cheol} and Jeong, {Taikyeong T.} and Lee, {Won Suk}",
year = "2006",
doi = "10.1007/11836810_30",
language = "English",
isbn = "3540383417",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "415--426",
booktitle = "Information Security - 9th International Conference, ISC 2006, Proceedings",
address = "Germany",
note = "9th International Information Security Conference, ISC 2006 ; Conference date: 30-08-2006 Through 02-09-2006",
}