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计算机工程 ›› 2011, Vol. 37 ›› Issue (23): 132-134. doi: 10.3969/j.issn.1000-3428.2011.23.045

• 安全技术 • 上一篇    下一篇

线性逼近SVM在入侵检测中的应用

李恒杰,郑伟强,蔺国梁   

  1. (甘肃联合大学电子信息工程学院,兰州 730000)
  • 收稿日期:2011-06-20 出版日期:2011-12-05 发布日期:2011-12-05
  • 作者简介:李恒杰(1965-),男,教授、CCF高级会员,主研方向:智能信息处理,网络安全;郑伟强,副教授、硕士;蔺国梁,讲师
  • 基金资助:
    甘肃省教育厅科研基金资助项目(0613B-03)

Application of Linear Approximation SVM in Intrusion Detection

LI Heng-jie, ZHENG Wei-qiang, LIN Guo-liang   

  1. (School of Electronics and Information Engineering, Gansu Lianhe University, Lanzhou 730000, China)
  • Received:2011-06-20 Online:2011-12-05 Published:2011-12-05

摘要: 引入分段线性识别算法,提出一种线性逼近支持向量机(SVM)入侵检测模型。将特征空间剖分成若干子空间,在每个子空间中基于SVM构造5个最优分类面,将各个分类面链接起来构成5个分片最优分类面以逼近理论上的最优分类超曲面。实验结果证明,该模型的训练时间较短,在噪声数据存在的情况下识别正确率较高。

关键词: 入侵检测系统, 分段线性识别, 线性逼近, 支持向量机, 特征空间

Abstract: Piecewise linear recognition algorithm is introduced in this paper, and an linear approximaiton Support Vector Machine(SVM) model for intrusion detection is proposed. In this model, the feature space is partitioned into several sub-space, the five best face are made in each sub-sector based on support vector classification, and then link together to form each of five categories face the optimal classification surface patch to approximate the theoretical optimal separating hyper surface. Experimental results show that the model needs short training time, and in the presence of noise data, it has high detection accuracy rate.

Key words: intrusion detection system, piecewise linear recognition, linear approximation, Support Vector Machine(SVM), feature space

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