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基于潜在语义模型的SVM入侵检测研究

Research on support vector machine for intrusion detection based on latent semantic model
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摘要 论文提出了一种基于潜在语义索引(LSI)和支持向量机(SVM)的异常入侵检测方法。选取PARPA’98BSM数据集作为训练数据和测试数据,通过实验比较和分析表明:基于LSI和SVM方法的入侵检测系统具有较高的检测率和较低的虚警率,且能大大减低计算的复杂性,是一种有效的异常识别和检测方法。 This paper proposes a new Support Vector Machine （SVM） for anomaly intrusion detection method based on Latent Semantic Indexing（LSI）.In this paper,the PARPA＇98 data sets are chosen as training and testing data sets,experiments show that our method has a higher detection rate and a lower false positive rate,and can greatly reduce the computation complexity.It is an effective anomaly identifying and detecting method.
作者 杨清 李方敏
机构地区 湖南科技大学计算机科学与工程学院 武汉理工大学信息工程学院
出处 《计算机工程与应用》 CSCD 北大核心 2007年第5期143-145,152,共4页 Computer Engineering and Applications
基金 湖南省杰出青年基金项目(the Science Fund of Hunan Province for Distinguished Young Scholar China under Grant No.03JJY1012)
关键词 入侵检测 支持向量机 潜在语义模型 intrusion detection Support Vector Machine latent semantic model
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
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参考文献12

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二级参考文献11

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计算机工程与应用
2007年 第5期

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