Research of Intrusion Detection Method Based on Ant Colony Clustering

DOI

10.2991/icmmct-16.2016.2How to use a DOI?

Keywords

intrusion detection; data mining; clustering analysis; ant colony clustering

Abstract

Network intrusion detection has been intensively investigated in recent years. In this paper, we propose an adaptive method based on ant colony clustering to discover unknown attacks. The focus of the method is the clustering process of an ant colony movement. The structure of the intrusion detection system based on ant colony clustering is designed. We use the KDD99 data set to design and evaluate our algorithm. The experimental results show the capability of our method successfully to detect network intrusions compared with the K-Means clustering algorithm. The method can not only improve the detection rate but also reduce false positive rate significantly，and can automatically detect various kinds of attacks.

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  - Qiang Yue
AU  - Zhongyu Hu
AU  - Shikai Shen
AU  - Dawei Zhang
PY  - 2016/03
DA  - 2016/03
TI  - Research of Intrusion Detection Method Based on Ant Colony Clustering
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 6
EP  - 11
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.2
DO  - 10.2991/icmmct-16.2016.2
ID  - Yue2016/03
ER  -