Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Ling Tan; Chong Li; Jingming Xia; Jun Cao

首页> 中文期刊> 《计算机、材料和连续体（英文）》 >Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

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摘要

Due to the widespread use of the Internet,customer information is vulnerable to computer systems attack,which brings urgent need for the intrusion detection technology.Recently,network intrusion detection has been one of the most important technologies in network security detection.The accuracy of network intrusion detection has reached higher accuracy so far.However,these methods have very low efficiency in network intrusion detection,even the most popular SOM neural network method.In this paper,an efficient and fast network intrusion detection method was proposed.Firstly,the fundamental of the two different methods are introduced respectively.Then,the selforganizing feature map neural network based on K-means clustering(KSOM)algorithms was presented to improve the efficiency of network intrusion detection.Finally,the NSLKDD is used as network intrusion data set to demonstrate that the KSOM method can significantly reduce the number of clustering iteration than SOM method without substantially affecting the clustering results and the accuracy is much higher than Kmeans method.The Experimental results show that our method can relatively improve the accuracy of network intrusion and significantly reduce the number of clustering iteration.

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《计算机、材料和连续体（英文）》 |2019年第7期|P.275-288|共14页
作者
Ling Tan; Chong Li; Jingming Xia; Jun Cao;
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作者单位

College of Computer and Software Nanjing University of Information Science&Technology Nanjing 210044 China;

College of Electronic and Information Nanjing University of Information Science&Technology Nanjing 210044 China;

College of Electronic and Information Nanjing University of Information Science&Technology Nanjing 210044 China;

School of Geography Geology and the Environment Keele University Keele UK;

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原文格式 PDF
正文语种 chi
中图分类计算技术、计算机技术;
关键词
K-means clustering; self-organizing feature map neural network; network security; intrusion detection; NSL-KDD data set;

机译：K-Means Clustering;自组织特征地图神经网络;网络安全;入侵检测;NSL-KDD数据集;

Application of Self-Organizing Feature Map Neural Network Based on K-means Clustering in Network Intrusion Detection

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