Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)

Efficient K-means Algorithm in Intrusion Detection

Authors
Wenjun Yang
Corresponding Author
Wenjun Yang
Available Online March 2017.
DOI
https://doi.org/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.
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Proceedings
2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
March 2017
ISBN
978-94-6252-324-1
ISSN
1951-6851
DOI
https://doi.org/10.2991/msam-17.2017.43How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Wenjun Yang
PY  - 2017/03
DA  - 2017/03
TI  - Efficient K-means Algorithm in Intrusion Detection
BT  - 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  - https://doi.org/10.2991/msam-17.2017.43
ID  - Yang2017/03
ER  -