doi: 10.7763/IJFCC.2012.V1.5
Anomaly Intrusion Detection based on Clustering a Data Stream
Jinsuk Kang
1
,
- 1 Jangwee Research Institute of National Defene, Ajou University, South Korea
- 2 Java Inc. Corp, South Korea.
Abstract
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.
Keywords
- Intrusion detection
- anomaly detection
- data mining
- clustering
- data stream
How to Cite
Jinsuk Kang and Sanghyun Oh, "Anomaly Intrusion Detection based on Clustering a Data Stream," International Journal of Future Computer and Communication, vol. 1, no. 1, pp. 17-20, 2012. https://doi.org/ 10.7763/IJFCC.2012.V1.5
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).