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2018 Volume 43 Issue 12
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ZOU Chen-song1, YANG Yu2. Intrusion Detection Method Based on Density and Optimal Clustering Number[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(12): 91-99. doi: 10.13718/j.cnki.xsxb.2018.12.016
Citation: ZOU Chen-song1, YANG Yu2. Intrusion Detection Method Based on Density and Optimal Clustering Number[J]. Journal of Southwest China Normal University(Natural Science Edition), 2018, 43(12): 91-99. doi: 10.13718/j.cnki.xsxb.2018.12.016
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Intrusion Detection Method Based on Density and Optimal Clustering Number

  • 1. Department of Electrical Engineering;
    2. Department of Mechanical Engineering, Guangdong Songshan Polytechnic College, Shaoguan Guangdong 512126, China

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  • Received Date: 15/05/2018
  • According to the problems of clustering algorithm in the application of intrusion detection, such as parameter presupposition, clustering effectiveness evaluation and unknown attack type detection, an improved algorithm based on density and optimal clustering number has been proposed. And according to the distribution of the samples, the initial clustering center has been determined heuristically, a new internal evaluation index has been proposed from the point of view of the geometric structure of the samples, and the optimal clustering number has been determined. On this basis, an incremental intrusion detection model has been designed to realize the dynamic adjustment of the clustering center and the number of clusters. Experimental results show that compared with K-means and other two improved clustering algorithms, the new algorithm has faster convergence speed and higher clustering accuracy, and can effectively cluster unknown network behaviors, and has better intrusion detection effect.
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Intrusion Detection Method Based on Density and Optimal Clustering Number

  • Received Date: 15/05/2018

Abstract: According to the problems of clustering algorithm in the application of intrusion detection, such as parameter presupposition, clustering effectiveness evaluation and unknown attack type detection, an improved algorithm based on density and optimal clustering number has been proposed. And according to the distribution of the samples, the initial clustering center has been determined heuristically, a new internal evaluation index has been proposed from the point of view of the geometric structure of the samples, and the optimal clustering number has been determined. On this basis, an incremental intrusion detection model has been designed to realize the dynamic adjustment of the clustering center and the number of clusters. Experimental results show that compared with K-means and other two improved clustering algorithms, the new algorithm has faster convergence speed and higher clustering accuracy, and can effectively cluster unknown network behaviors, and has better intrusion detection effect.

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