Research of Intrusion Detection Method Based on Ant Colony Clustering
Qiang Yue, Zhongyu Hu, Shikai Shen, Dawei Zhang
Available Online March 2016.
- 10.2991/icmmct-16.2016.2How to use a DOI?
- intrusion detection; data mining; clustering analysis; ant colony clustering
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 significantlyï¼Œand can automatically detect various kinds of attacks.
- © 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/).
Cite this article
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 -