Online Anomaly Detection Based on Support Vector Clustering

DOI

10.1080/18756891.2015.1061393How to use a DOI?

Keywords

Online anomaly detection, support vector clustering, self-organizing map, quadratic programming

Abstract

A two-phase online anomaly detection method based on support vector clustering (SVC) in the presence of non-stationary data is developed in this paper which permits arbitrary-shaped data clusters to be precisely treated. In the first step, offline learning is performed to achieve an appropriate detection model. Then the current model dynamically evolves to match the rapidly changing real-world data. To reduce the dimension of the quadratic programming (QP) problem emerging in the SVC, self-organizing map (SOM) and a replacement mechanism are used to summarize the incoming data. Thus, the proposed method can be efficiently and effectively useable in real time applications. The performance of the proposed method is evaluated by a simulated dataset, three subsets extracted from the KDD Cup 99 dataset, and the keystroke dynamics dataset. Results illustrate capabilities of the proposed method in detection of new attacks as well as normal pattern changes over the time.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - JOUR
AU  - Mohammad Amin Adibi
AU  - Jamal Shahrabi
PY  - 2015
DA  - 2015/08/01
TI  - Online Anomaly Detection Based on Support Vector Clustering
JO  - International Journal of Computational Intelligence Systems
SP  - 735
EP  - 746
VL  - 8
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1061393
DO  - 10.1080/18756891.2015.1061393
ID  - Adibi2015
ER  -