| 异常检测中支持向量机最优模型选择方法 |
| |
| 引用本文： | 张雪芹,顾春华,吴吉义. 异常检测中支持向量机最优模型选择方法[J]. 电子科技大学学报(自然科学版), 2011, 40(4): 559-563. DOI: 10.3969/j.issn.1001-0548.2011.04.017 |
| |
| 作者姓名： | 张雪芹 顾春华 吴吉义 |
| |
| 作者单位： | 1.华东理工大学信息科学与工程学院 上海 徐汇区 200237; |
| |
| 基金项目： | 国家自然科学基金(60773094) |
| |
| 摘 要： | 为了构建一个具有良好的学习性能和推广能力的异常检测分类器,在结构风险最小(SRM)原则下讨论了基于支持向量机(SVM)的异常检测分类器的设计准则,提出了SVM分类器模型及其参数快速选择和评估方法,并给出了异常检测分类器训练步骤.针对KDD'99网络入侵检测数据集,实验结果表明,该方法能够有效地缩短入侵检测分类模型建立时...
|
| 关 键 词： | 异常检测 模型选择 参数估计 结构风险 支持向量机 |
| 收稿时间： | 2009-12-04 |
Support Vector Machine Based Optimal Model Selection Method in Anomaly Detection |
| |
| Affiliation: | 1.School of Information Science and Engineering,East China University of Science and Technology Xuhui Shanghai 200237;2.Hangzhou Key Lab of E-Business and Information Security,Hangzhou Normal University Hangzhou 310036 |
| |
| Abstract: | In order to construct an anomaly detection classifier which has good learning and generalization ability, under the structural risk minimization (SRM) principle，the design rules of a support vector machines (SVMs) based anomaly detection classifier is discussed. The model and its parameters selection and estimation method of a SVM classifier are proposed. The training steps of a SVM anomaly detection classifier are given. Experiments on KDD’99 network intrusion detection dataset indicate that the proposed methods can speed up the process of constructing an intrusion detection classifier and the classification accuracy is higher. |
| |
| Keywords: | |
|
| 点击此处可从《电子科技大学学报(自然科学版)》浏览原始摘要信息 |
|
点击此处可从《电子科技大学学报(自然科学版)》下载全文 |
