Efficient K-means Algorithm in Intrusion Detection

DOI

10.2991/msam-17.2017.43How to use a DOI?

Keywords

intrusion detection, K-means, cluster, average density

Abstract

In order to improve the detection rate of invasion, reduce false detection rate and put forward a method based on density and maximum distance of k means clustering algorithm, the clustering results used in intrusion detection, improved the original algorithm in the choice of initial clustering center, simplify the computational complexity of the algorithm. Finally simulation experiments using KDD Cup 99 data set. Results show that the model can obtain ideal intrusion detection rate and false detection rate.

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  - CONF
AU  - Wenjun Yang
PY  - 2017/03
DA  - 2017/03
TI  - Efficient K-means Algorithm in Intrusion Detection
BT  - Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
PB  - Atlantis Press
SP  - 193
EP  - 195
SN  - 1951-6851
UR  - https://doi.org/10.2991/msam-17.2017.43
DO  - 10.2991/msam-17.2017.43
ID  - Yang2017/03
ER  -