%A XIAO Miaomiao, WEI Benzheng, YIN Yilong
%T A hybrid intrusion detection system based on BFOA and *K*-means algorithm
%0 Journal Article
%D 2018
%J Journal of Shandong University(Engineering Science)
%R 10.6040/j.issn.1672-3961.0.2017.428
%P 115-119
%V 48
%N 3
%U {http://gxbwk.njournal.sdu.edu.cn/CN/abstract/article_1744.shtml}
%8 2018-06-20
%X The *K*-means algorithm was sensitive to the selection of the initial clustering center and the number of clusters *K*, which led to the instability of the clustering results and would have a significant impact on the detection results of IDS(instrusion detection system, briefly named as IDS). To solve this problem, a hybrid intrusion detection algorithm(HIDS)based on BFOA(bacterial foraging optimization algorithm)and *K*-means was proposed. The value of *K* could be determined dynamically based on the distance threshold method. BFOA could be used to optimize the initial cluster centers, which made the initial clustering centers to be globally optimal. Therefore, the instability of the clustering results of *K*-means algorithm was solved. The detection rate was 98.33% by performing an experimental test on the KDD99 dataset. The experimental results showed that the method could effectively improve the detection rate and reduce the false detection rate.