JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2018, Vol. 48 ›› Issue (3): 115-119.doi: 10.6040/j.issn.1672-3961.0.2017.428

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A hybrid intrusion detection system based on BFOA and K-means algorithm

XIAO Miaomiao1,2, WEI Benzheng1,2*, YIN Yilong3   

  1. 1. College of Science and Technology, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, China;
    2. Computational Medicine Lab, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong, China;
    3. School of Software Engineering, Shandong University, Jinan 250101, Shandong, China
  • Received:2017-05-05 Online:2018-06-20 Published:2017-05-05

Abstract: 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.

Key words: intrusion detection, bacterial foraging optimization algorithm, HIDS, K-means algorithm, detection rate

CLC Number: 

  • TP391
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