Computer Science ›› 2018, Vol. 45 ›› Issue (2): 226-230.doi: 10.11896/j.issn.1002-137X.2018.02.039

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PCA-AKM Algorithm and Its Application in Intrusion Detection System

NIU Lei and SUN Zhong-lin   

  • Online:2018-02-15 Published:2018-11-13

Abstract: The initial clustering center is the point or object selected for the first time in the clustering process.Aiming at the instability of clustering results in traditional K-means algorithm caused by choosing the initial clustering centers randomly,the PCA-AKM algorithm was proposed.The algorithm uses the principal component analysis to extract the main components of the data set to achieve data dimensionality reduction,and then uses the self-defined indicators Dw to choose the initial clustering centers,avoiding the clustering center local optimum.Comparison with the K-means algorithm on the UCI data set proves that the clustering stability of the PCA-AKM algorithm is higher than that of K-means.Experiment proves that the algorithm has high detection rate and low false detection rate on KDD CUP99 data set when it is used to simulate intrusion detection,and the algorithm can improve the accuracy of intrusion detection effectively.

Key words: K-means algorithm,Principal component analysis,Dw,Intrusion detection

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