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基于数据流模糊聚类挖掘的入侵检测系统研究

Intrusion Detection System Based on Fuzzy Clustering Mining of Date Stream
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摘要 传统的基于数据挖掘入侵检测技术往往是基于静态数据的检测,随着网络速度的提高和网络流量的剧增,网络数据通常以数据流的形式出现。提出了一种作用于数据流的模糊聚类挖掘算法(SFCM),并且针对该算法提出了一种基于数据流模糊聚类的入侵检测系统,实验结果显示,该方法有较高的检测率和较低的漏报率和误报率。 Detection technologies based on data mining are often based on static data test. With the increase of network speed and traffic, the network data usually appear in the form of data flow. A fuzzy clustering mining of data stream（ SFCM） algorithm which acts on data flow is proposed, and an intrusion detection system based on fuzzy clustering mining of data stream is proposed based on this algorithm. The experimental results show that this method has a high detection rate and low false negative rate and false positive rate.
作者 李俊鹏 王勇 白焱 李云杰
机构地区 空军工程大学航空航天工程学院
出处 《现代防御技术》 北大核心 2013年第2期207-211,共5页 Modern Defence Technology
关键词 数据流挖掘 聚类算法 基于数据流的模糊聚类挖掘算法(SFCM) 入侵检测 data stream mining clustering algorithm fuzzy clustering mining of data stream （SF- CM） intrusion detection
分类号 TP391.9 [自动化与计算机技术—计算机应用技术] TP273.4 [自动化与计算机技术—检测技术与自动化装置]
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参考文献9

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现代防御技术
2013年 第2期

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