A hybrid approach for anomaly detection using K-means and PSO

DOI

10.2991/icence-16.2016.151How to use a DOI?

Keywords

Anomaly detection, Particle Swarm Optimization, K-Means, Clustering analysis.

Abstract

The network intrusion detection systems which based on anomaly detection techniques plays an important role in protection network and systems from harmful attacks. With increasing in attacks and the new security challenges, The lower accuracy of anomaly detection method based on cluster analysis network traffic is a big question, In this paper, we proposed a hybrid anomaly detection method by combining the Particle Swarm Optimization(PSO) and K-Means clustering algorithms improving the accuracy. We first preprocess features of data traffic, extract the characteristics of the various categories of attack, and then use parallel PSO calculation, to find the best or a little approximations to optimal clustering initial point. Finally, we perform the K-Means clustering algorithm. Experiment results show the effectiveness of the proposed optimization scheme.

Copyright

© 2016, 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/).

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TY  - CONF
AU  - Ke-Wei Wang
AU  - Su-Juan Qin
PY  - 2016/09
DA  - 2016/09
TI  - A hybrid approach for anomaly detection using K-means and PSO
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 821
EP  - 826
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.151
DO  - 10.2991/icence-16.2016.151
ID  - Wang2016/09
ER  -