The application of Fuzzy clustering number algorithm in network intrusion detection
- https://doi.org/10.2991/iccsee.2013.785How to use a DOI?
- K-means algorithm, Fuzzy clustering number, Intrusion detection
In view of the defects of K-means algorithm in intrusion detection: the need of preassign cluster number and sensitive initial center and easy to fall into local optimum, this paper puts forward a fuzzy clustering algorithm. The fuzzy rules are utilized to express the invasion features, and standardized matrix is adopted to further process so as to reflect the approximation degree or correlation degree between the invasion indicator data and establish a similarity matrix. The simulation results of KDD CUP1999 data set show that the algorithm has better intrusion detection effect and can effectively detect the network intrusion data.
- © 2013, 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 - Gua Lang PY - 2013/03 DA - 2013/03 TI - The application of Fuzzy clustering number algorithm in network intrusion detection BT - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) PB - Atlantis Press SP - 2967 EP - 2969 SN - 1951-6851 UR - https://doi.org/10.2991/iccsee.2013.785 DO - https://doi.org/10.2991/iccsee.2013.785 ID - Lang2013/03 ER -