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Security detection of network intrusion: application of cluster analysis method
W.H. Yang 1

Railway Signal and Information Engineering Department, Shandong Polytechnic, Jinan, Shandong 250104, China

 PDF, 868 kB

DOI: 10.18287/2412-6179-CO-657

Pages: 660-664.

Full text of article: English language.

In order to resist network malicious attacks, this paper briefly introduced the network intrusion detection model and K-means clustering analysis algorithm, improved them, and made a simulation analysis on two clustering analysis algorithms on MATLAB software. The results showed that the improved K-means algorithm could achieve central convergence faster in training, and the mean square deviation of clustering center was smaller than the traditional one in convergence. In the detection of normal and abnormal data, the improved K-means algorithm had higher accuracy and lower false alarm rate and missing report rate. In summary, the improved K-means algorithm can be applied to network intrusion detection.

clustering analysis, K-means, cross entropy, network intrusion.

Yang WH. Security detection of network intrusion: application of cluster analysis method. Computer Optics 2020; 44(4): 660-664. DOI: 10.18287/2412-6179-CO-657.


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