[1]李建 李杰 孙燕花.基于聚类融合的入侵检测[J].计算机技术与发展,2011,(10):250-253.
　LI Jian,LI Jie,SUN Yan-hua.An Intrusion Detection Based on Clustering Ensemble[J].,2011,(10):250-253.
点击复制

基于聚类融合的入侵检测()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年10期
页码:
250-253
栏目:
安全与防范
出版日期:
1900-01-01

文章信息/Info

Title:
An Intrusion Detection Based on Clustering Ensemble
文章编号:
1673-629X（2011）10-0250-04
作者:
李建 李杰 孙燕花
中南大学信息科学与工程学院
Author(s):
LI Jian;  LI Jie;  SUN Yan-hua
School of Imformation Science and Engineering ,Central South University
关键词:
网络安全; 入侵检测; 聚类融合
Keywords:
network security ;  intrusion detection ;  clustering ensemble
分类号:
TP393.08
文献标志码:
A
摘要:
随着互联网的飞速发展，网络安全的问题日趋严重，传统的网络安全技术已难以应对日益繁多的网络攻击。因此入侵检测便应运而生了，而且其重要性日益提高。基于聚类分析的入侵检测已经成为其主要研究方向。聚类分析是一种有效的异常入侵检测方法，可用以在网络数据集中区分正常流量和异常流量。但单一的聚类算法很难达到预期的效果，为了提高入侵检测的效果，文中采用聚类融合技术，提出一种基于Co—assocition的模糊聚类融合算法，通过实验检测能显著提高检测率和降低误报率
Abstract:
With the rapid development of network, more and more network security problems are appearing, the traditional network security technology has been difficult to protect the network by growing range of network attacks. So the intrusion detection is turned out, and it gets more important in the network. Intrusion detection based on cluster analysis has become the main research directions. Cluster analysis is an effective method for anomaly intrusion detection, and it can distinguish the normal and abnormal data of the network data. But a single clustering algorithm is hard to achieve the desired effect. In order to improve the effectiveness of intrusion detection, proposes a new fuzzy clustering ensemble algorithm based On Co-assocition . Through experimental testing can significantly improve the detection rate and lower false alarm rate

相似文献/References:

[1]李雷 丁亚丽 罗红旗.基于规则约束制导的入侵检测研究[J].计算机技术与发展,2010,(03):143.
　LI Lei,DING Ya-li,LUO Hong-qi.Intrusion Detection Technology Research Based on Homing - Constraint Rule[J].,2010,(10):143.
[2]马志远,曹宝香.改进的决策树算法在入侵检测中的应用[J].计算机技术与发展,2014,24(01):151.
　MA Zhi-yuan,CAO Bao-xiang.Application of Improved Decision Tree Algorithm in Intrusion Detection System[J].,2014,24(10):151.
[3]高峥 陈蜀宇 李国勇.混合入侵检测系统的研究[J].计算机技术与发展,2010,(06):148.
　GAO Zheng,CHEN Shu-yu,LI Guo-yong.Research of a Hybrid Intrusion Detection System[J].,2010,(10):148.
[4]林英 张雁 欧阳佳.日志检测技术在计算机取证中的应用[J].计算机技术与发展,2010,(06):254.
　LIN Ying,ZHANG Yan,OU Yang-jia.Application of Log Testing Technology in Computer Forensics[J].,2010,(10):254.
[5]李钦 余谅.基于免疫遗传算法的网格入侵检测模型[J].计算机技术与发展,2009,(05):162.
　LI Qin,YU Liang.Grid Intrusion Detection Model Based on Immune Genetic Algorithm[J].,2009,(10):162.
[6]黄世权.网络存储安全分析[J].计算机技术与发展,2009,(05):170.
　HUANG Shi-quan.Analysis of Network Storage＇s Safety[J].,2009,(10):170.
[7]李睿 肖维民.基于孤立点挖掘的异常检测研究[J].计算机技术与发展,2009,(06):168.
　LI Rui,XIAO Wei-min.Research on Anomaly Intrusion Detection Based on Outlier Mining[J].,2009,(10):168.
[8]胡琼凯 黄建华.基于协议分析和决策树的入侵检测研究[J].计算机技术与发展,2009,(06):179.
　HU Oiong-kai,HUANG Jian-hua.Intrusion Detection Based on Protocol Analysis and Decision Tree[J].,2009,(10):179.
[9]汪世义.基于优化支持向量机的网络入侵检测技术研究[J].计算机技术与发展,2009,(07):177.
　WANG Shi-yi.Network Intrusion Detection Based on Improved Support Vector Machine[J].,2009,(10):177.
[10]严华 蔡瑞英.即时通信监控系统的设计与实现[J].计算机技术与发展,2009,(07):242.
　YAN Hua,CAI Rui-ying.Design and Implementation of Monitoring System of Instant Messaging[J].,2009,(10):242.
[11]李生 邓一贵 唐学文 潘磊 林玉香.基于移动代理的分布式入侵检测系统的研究[J].计算机技术与发展,2009,(09):132.
　LI Sheng,DENG Yi-gui,TANG Xue-wen,et al.Research of Mobile Agent - Based Distributed Intrusion Detection System[J].,2009,(10):132.
[12]邵晓宇 杨善林 褚伟.基于Linux入侵检测动态防火墙的设计与实现[J].计算机技术与发展,2008,(05):156.
　SHAO Xiao-yu,YANG Shan-lin,CHU Wei.Design and Implementation of Dynamic Intrusion Detection Firewall Based on Linux[J].,2008,(10):156.
[13]李守国 李俊.基于数据挖掘的入侵检测系统设计[J].计算机技术与发展,2006,(04):212.
　LI Shou-guo,LI Jun.Design of Data Mining Based Intrusion Detection System[J].,2006,(10):212.
[14]陈建锐 何增颖 梁永成.IPv6网络入侵检测系统设计[J].计算机技术与发展,2010,(09):123.
　CHEN Jian-rui,HE Zeng-ying,LIANG Yong-cheng.Design of Network Intrusion Detection System on IPv6[J].,2010,(10):123.
[15]王峰 宗平.面向混合入侵检测策略的应用模型研究[J].计算机技术与发展,2011,(07):149.
　WANG Feng,ZONG Ping.Study of Mixed Model Oriented Intrusion Detection[J].,2011,(10):149.
[16]谢振国 凌捷.网络安全预警系统的研究[J].计算机技术与发展,2011,(11):250.
　XIE Zhen-guo,LING Jie.Study of a Network Security and Early-Warning System[J].,2011,(10):250.
[17]陈剑,蔡龙征.一种无监督异常入侵检测的簇异常度量方法[J].计算机技术与发展,2013,(04):131.
　CHEN Jian,CAI Long-zheng.A Cluster Anomaly Measure Approach for Unsupervised Anomaly Intrusion Detection[J].,2013,(10):131.
[18]姜参,王大伟.一种改进蚁群聚类的入侵检测方法[J].计算机技术与发展,2013,(12):139.
　JIANG Shen,WANG Da-wei.An Improved Ant Colony Clustering Method for Intrusion Detection[J].,2013,(10):139.
[19]张公让,万飞. 基于网格搜索的 SVM 在入侵检测中的应用[J].计算机技术与发展,2016,26(01):97.
　ZHANG Gong-rang,WAN Fei. Application of Support Vector Machine in Network Intrusion Detection Based on Grid Search[J].,2016,26(10):97.
[20]严佩敏,姚嘉豪.SDN 下基于入侵检测的主动蜜网[J].计算机技术与发展,2021,31(增刊):96.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 019]
　YAN Pei-min,YAO Jia-hao.Active Honeynet Based on Intrusion Detection System in Software Defined Network[J].,2021,31(10):96.[doi:10. 3969 / j. issn. 1673-629X. 2021. S. 019]

备注/Memo

备注/Memo:
李建（1986-），男，湖南湘潭人，硕士研究生，研究方向为网络管理；李杰，教授，研究方向为网络管理
更新日期/Last Update: 1900-01-01