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基于最小二乘隐空间支持向量机的IDS检测算法的设计 被引量：3

Algorithm Design of IDS Based on Least Squares Hidden Space Support Vector Machines
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摘要 在基于支持向量机的基础上,提出一种新的利用最小二乘隐空间支持向量机设计IDS的检测算法,解决了网络入侵检测系统中检测算法的分类精度不高、训练样本数需要较多,及训练学习时间较长等问题.仿真实验结果表明,本算法较基于支持向量机的检测算法具有更快的收敛性、更快的迭代速度、更高的检测精度和更低的误报率. A new algorithm based on least squares hidden space support vector machines（LSHSSVMs） was proposed,to improve classific precision of network intrusion detection model,reduce the number of training data set and learning time.The experimental results useing KDD CUP 1999 data set show that LSHSSVMs has better generalization ability,quicker iterative speed,higher detection accuracy,and lower error rate than SVMs.
作者 张家超 孔媛媛 刘飞
机构地区 连云港职业技术学院信息工程学院 兰州交通大学电子与信息工程学院
出处 《微电子学与计算机》 CSCD 北大核心 2008年第11期171-173,177,共4页 Microelectronics & Computer
基金 江苏省教育厅资助项目(2005-290) 江苏省教科院资助项目(2005-R-196)
关键词 网络安全 入侵检测 最小二乘隐空间支持向量机 算法设计 network security intrusion detection least squares hidden space support vector machines（LSHSSVMs） algorithm design
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献8

  • 1张家超.利用支持向量回归机设计IDS的检测算法[J].计算机应用,2008,28(3):609-611. 被引量：6
  • 2张家超.利用Lagrange支持向量回归机设计IDS的检测算法[J].计算机工程与应用,2008,44(19):118-120. 被引量：1
  • 3Vapnik V N. The nature of statistical learning theory[M]. New York:Springer Verlag, 1999,
  • 4Zhang Li, Zhou Wei - da, Jiao Li - cheng. Hidden space support vector machines[J ]. IEEE Transactions on Neural Networks,2004,15(6) :1424 - 1434.
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  • 6王玲,薄列峰,刘芳,焦李成.最小二乘隐空间支持向量机[J].计算机学报,2005,28(8):1302-1307. 被引量：12
  • 7Suykens J A K, Lukas L, Dooren P Van, et al. Least squares support vector machines classifiers: a large scale algorithm[C]// Proceedings of the European Conference on Circuit Theory and Design. Italy Stresa, 1999:839 - 842.
  • 8KDD Cup99 dataset[EB/OL]. [ 1999 - 10 - 19]. http:// kdd. ics. uci. edu/databases/kddcup99/kddcup99. htm.

二级参考文献39

  • 1段丹青,陈松乔,杨卫平.网络入侵检测中的支持向量机主动学习算法[J].计算机工程与应用,2006,42(1):117-119. 被引量：5
  • 2张琨,曹宏鑫,严悍,刘凤玉.支持向量机在网络异常入侵检测中的应用[J].计算机应用研究,2006,23(5):98-100. 被引量：9
  • 3Theuns V,Ray H.Intrusion detection techniques and approaches[J]. Computer Communication, 2002,25 ( 15 ) : 1356-1365.
  • 4Bonifacio J M,Cansian A M,Carvalho A C,et al.Neural networks applied in intrusion detection systems[C]//Proceedings of the IEEE World Congress on Computational Intelligence.Oakland, CA:IEEE Computer Society Press, 1998:205-210.
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  • 7Vapnik V N.The nature of statistical learning theory[M].New York: Springer-Verlag, 1999.
  • 8Vapnik V N.An overview of statistical learning theory[J].IEEE Trans on Neural Network, 1999, 10(3 ) : 988-999.
  • 9Mangasarian O L,Musieant D R.Lagrangian support vector machines[J].Journal of Machine Learning Research, 2001 ( 1 ) : 161 - 177.
  • 10Shao Xiao-jian,He Guo-ping.Lagrange support vector regression[J]. IIMST, 2005,3 ( 1 ) : 434-440.

共引文献15

同被引文献16

  • 1陈旸,刘泽民.基于卡尔曼滤波的盲空时检测器[J].微电子学与计算机,2007,24(1):130-132. 被引量：1
  • 2Almgren M, Jonsson E. Using active learning in intrusion detection [C]//Proceedings of the 17th IEEE Symposium on Security Foundations Workshop. IEEE Computer Society Press. Sweden: Goteborg, 2004:88-98.
  • 3Vapnik V N. The nature of statistical learning theory[ M]. New York: Springer - verlag, 1999.
  • 4Tong S, Koller D. Support vector machine active learning with applications to text classification[J ]. Machine learningresearch, 2001(2):45 - 66.
  • 5Girolami M. Mercer kemel based clustering in feature space[J]. IEEE Transactionson Neural Networks, 2002, 13 (3):780 - 784.
  • 6Jiang Wei, Zhao Xia, Chen Qijun. Kalman filtering de sign based on real-time updating of noise matrix[C]// International Joint Conference on Bioinformatics Wash- ington: IEEE, 2009: 569-572.
  • 7JosC A. Ramos, Erik I. Verriest. Total least squares fitting of two point sets in m-D[C]// Conference on Decision & Control. San Diego: IEEE, 1997: 5048 -5053.
  • 8Munoz Hernandez G A, Diaz Sanchez A, Jones D I, et al. Estimating the frequency variation of the mexican grid by Kalman filtering [C]//IEEE International Conference on Electrical, Communications, and Computers. Washington: IEEE, 2009: 245-249.
  • 9Zhang Z G, Chan S C, Tsui K M. A recursive frequency estimator using linear prediction and a kalman-filter-based iterative algorithm [J]. IEEE Transaction on Circults and Systems, 2008, 55(6): 576-580.
  • 10CristianiniN Shawe-TaylorJ 李国正译.支持向量机导论[M].北京:电子工业出版社,2004..

引证文献3

二级引证文献8

微电子学与计算机
2008年 第11期

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