| 基于密度聚类算法的入侵检测研究 |
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| 引用本文： | 蔡伟鸿,刘震. 基于密度聚类算法的入侵检测研究[J]. 计算机工程与应用, 2005, 41(21): 149-151 |
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| 作者姓名： | 蔡伟鸿 刘震 |
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| 作者单位： | 广东汕头大学工学院计算机系,汕头,505063;广东汕头大学工学院计算机系,汕头,505063 |
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| 摘 要： | 本文联系异常检测和数据挖掘,从理论上着重分析了在入侵检测系统中应用基于密度聚类算法的必要性和有效性,从TCPDump网络数据和系统日志中提取分析后生成特征数据,通过Clenmine中CEMI实现定制的基于密度的改进DBSCAN算法进行测试,结果表明利用该算法可以较好地识别分布式拒绝服务攻击等多种入侵行为。
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| 关 键 词： | 异常检测 基于密度的聚类 数据挖掘 |
| 文章编号： | 1002-8331-(2005)21-0149-03 |
Intrusion Detection Research Based on the Density Clutering Method |
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| Cai Weihong,Liu Zhen. Intrusion Detection Research Based on the Density Clutering Method[J]. Computer Engineering and Applications, 2005, 41(21): 149-151 |
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| Authors: | Cai Weihong Liu Zhen |
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| Abstract: | In this paper,we have discussed the anomaly detection with the data mining.And theoretically we have analyzed the feasibility and necessity of using the density clustering method in the intrusion detection system.Using the specific CEMI interface offered by the Clenmine,we can implement the improved DBSCAN method.With the characteristic data distilling from a mass of the network TCPDump and system log,we have done a experiment.The result testified the intrusion detection system based on the density clustering method can identify the Ddos detection effectively. |
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| Keywords: | anomaly detection density clustering Data Mining |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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