期刊文献+

基于δ-HSSVM的协同入侵检测方法 被引量:2

Cooperative Intrusion Detection Method Based on δ-HSSVM
下载PDF
导出
摘要 传统入侵检测方法检测速度较慢。为此,提出一种基于δ-超球结构支持向量机的协同入侵检测方法。增加参数δ,以控制超球内样本的数量,按照网络协议产生3类基于δ-超球结构支持向量机的检测代理,以协同的方式对其相应网络数据做出决策。实验结果表明,与单一检测代理方法相比,该方法检测率高、检测时间少。 For the problem of slow detection speed of traditional intrusion detection,δ-hypersphere Structure Support Vector Machine(δ-HSSVM) is presented.The number of samples inside hypersphere are controled by the increasing parameter δ.Three detection agents based on δ-HSSVM generated according to network protocols.And the three detection agents make decisions to the corresponding network data.Experimental results show that,this method has a higher detection rate and less detection time than single detection agent.
出处 《计算机工程》 CAS CSCD 北大核心 2011年第21期100-101,104,共3页 Computer Engineering
基金 广东省自然科学基金资助项目(06021484 9151009001000007 9451009001002777) 广东省科技计划基金资助项目(2008A060201011) 广州市越秀区科技计划基金资助项目(2007-GX-075)
关键词 入侵检测 网络协议 检测代理 检测率 intrusion detection network protocol detection agent detection rate
  • 相关文献

参考文献4

二级参考文献25

  • 1张连华,张冠华,张洁,白英彩.基于粗糙集分类的网络入侵检测[J].上海交通大学学报,2004,38(z1):194-199. 被引量:1
  • 2赵宇,奚宏生,王子磊,杨坚.基于在线SVM的多用户检测算法及仿真[J].系统仿真学报,2006,18(1):50-53. 被引量:9
  • 3郭辉,刘贺平,王玲.最小二乘支持向量机参数选择方法及其应用研究[J].系统仿真学报,2006,18(7):2033-2036. 被引量:103
  • 4朱美琳,杨佩.基于支持向量机的多分类增量学习算法[J].计算机工程,2006,32(17):77-79. 被引量:11
  • 5Liu Cuijuan.The Application of Rough Sets on Network Intrusion Detection[C] //Proc.of the 6th International Conference on Machine Learning and Cybernetics.Hong Kong,China:[s.n.] ,2007.
  • 6Ziarko W.Incremental Learning with Hierarchies of Rough Decision Tables[C] //Proc.of 2004 IEEE Annual Meeting of the Fuzzy Information.[S.1.] :IEEE Press,2004.
  • 7Vapnik V. The nature of statistical learning theory. New York: Springer-Verlag, 1995, 5-13.
  • 8Burges C J C, Scholkopf B. Improving the accuracy and speed of support vector learning machines.Advances in Neural Information Processing Systems 9. Cambridge: MIT Press, 1997: 375-381.
  • 9Blanz V, Scholkopf B, Bultho H, et al. Comparison of view-based object recognition algorithms usingrealistic 3D models. Artificial Neural Networks - ICANN'96. Berlin: Springer Lecture Notes in Computer Science, 1996: 251-256.
  • 10Joachims T. Text categorization with support vector machines: Learning with many relevant features.Proceedings of the European Conference on Machine Learning. Berlin: Springer, 1998:137-142.

共引文献63

同被引文献10

  • 1LIU Dong-xia, ZHANG Yong-bo. An Intrusion Detection System Based on Honeypot Technology[C]. 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE), 2012:451-454.
  • 2Q. Hu, D. Yu, Z. Xie, J. Liu. Fuzzy Probabilistic Approxima- tion Spaces and Their Information Measures[J]. IEEE Trans- actions on Fuzzy Systems, 2006, 14(2) : 191-201.
  • 3MEI Xue, LING Hai-bin. Robust Visual Tracking and Vehi- cle Classification Via Linear Representation[J]. IEEE Transac- tions on Pattern Analysis and Machine Intellgence, 2011, 33 (11): 2259-2272.
  • 4ZHANG Lei, YANG Meng, FENG Xiang-chu[C]. Sparse Re- presentation or Collaborative Representation: Which Helps Face Recognition. In ICCV 2011.
  • 5J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, S. Yah, Sparse Representation for Computer Vision and Pattern Recognition[J]. Proc. IEEE, 2010,98(6):1031-1044.
  • 6周鲜成,申群太,王俊年.基于微粒群的K均值聚类算法在图像分类中的应用[J].小型微型计算机系统,2008,29(2):333-336. 被引量:10
  • 7吴证,周越,杜春华,袁泉,戈新良.彩色图像人脸特征点定位算法研究[J].电子学报,2008,36(2):309-313. 被引量:10
  • 8左萍平,孙赟,顾弘,齐冬莲.基于SMO的层次型1-FSVM算法[J].计算机工程,2010,36(19):188-189. 被引量:3
  • 9顾亦然,闵瑞,王保云.三维人脸姿态校正算法研究[J].仪器仪表学报,2010,31(10):2291-2295. 被引量:5
  • 10李文华.基于聚类分析的网络入侵检测模型[J].计算机工程,2011,37(17):96-98. 被引量:12

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部