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题目: Anomaly intrusion Detection Based on PLS Feature Extraction and Core Vector Machine 杂志: Knowledge-Based Systems Volume 40, March 2013 |
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pjstang
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2楼2013-03-27 05:09:05
slimmy
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【答案】应助回帖
感谢参与，应助指数 +1
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Anomaly intrusion detection based on PLS feature extraction and core vector machine 作者: Gan, XS (Gan Xu-sheng)[ 1,2 ] ; Duanmu, JS (Duanmu Jing-shun)[ 2 ] ; Wang, JF (Wang Jia-fu)[ 3 ] ; Cong, W (Cong Wei)[ 3 ] 来源出版物: KNOWLEDGE-BASED SYSTEMS 卷: 40 页: 1-6 DOI: 10.1016/j.knosys.2012.09.004 出版年: MAR 2013 被引频次: 0 (来自 Web of Science) 引用的参考文献: 19 [ 查看 Related Records ] 引证关系图 摘要: To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Partial Least Square (PLS) feature extraction and Core Vector Machine (CVM) algorithms. Principal elements are firstly extracted from the data set using the feature extraction of PLS algorithm to construct the feature set, and then the anomaly intrusion detection model for the feature set is established by virtue of the speediness superiority of CVM algorithm in processing large-scale sample data. Finally, anomaly intrusion actions are checked and judged using this model. Experiments based on KDD99 data set verify the feasibility and validity of the combined algorithm. (C) 2012 Elsevier B.V. All rights reserved. 入藏号: WOS:000315325800001 文献类型: Article 语种: English 作者关键词: Core vector machine; Partial least square; Feature extraction; Anomaly intrusion detection; Support Vector Machine 通讯作者地址: Gan, XS (通讯作者),AF Engn Univ, Equipment Management & Safety Engn Coll, Xian 710038, Shaanxi, Peoples R China. 地址: [ 1 ] Xijing Coll, Xian 710123, Shaanxi, Peoples R China [ 2 ] AF Engn Univ, Equipment Management & Safety Engn Coll, Xian 710038, Shaanxi, Peoples R China [ 3 ] AF Engn Univ, Coll Engn, Xian 710038, Shaanxi, Peoples R China 电子邮件地址: Ganxsheng123@163.com 出版商: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS Web of Science 类别: Computer Science, Artificial Intelligence 研究方向: Computer Science IDS 号: 095HR ISSN: 0950-7051 |
3楼2013-03-27 08:35:12
nuaawq
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【答案】应助回帖
感谢参与，应助指数 +1
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Anomaly intrusion detection based on PLS feature extraction and core vector machine 作者: Gan, XS (Gan Xu-sheng)[ 1,2 ] ; Duanmu, JS (Duanmu Jing-shun)[ 2 ] ; Wang, JF (Wang Jia-fu)[ 3 ] ; Cong, W (Cong Wei)[ 3 ] 来源出版物: KNOWLEDGE-BASED SYSTEMS 卷: 40 页: 1-6 DOI: 10.1016/j.knosys.2012.09.004 出版年: MAR 2013 被引频次: 0 (来自 Web of Science) 引用的参考文献: 19 [ 查看 Related Records ] 引证关系图 摘要: To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Partial Least Square (PLS) feature extraction and Core Vector Machine (CVM) algorithms. Principal elements are firstly extracted from the data set using the feature extraction of PLS algorithm to construct the feature set, and then the anomaly intrusion detection model for the feature set is established by virtue of the speediness superiority of CVM algorithm in processing large-scale sample data. Finally, anomaly intrusion actions are checked and judged using this model. Experiments based on KDD99 data set verify the feasibility and validity of the combined algorithm. (C) 2012 Elsevier B.V. All rights reserved. 入藏号: WOS:000315325800001 文献类型: Article 语种: English 作者关键词: Core vector machine; Partial least square; Feature extraction; Anomaly intrusion detection; Support Vector Machine 通讯作者地址: Gan, XS (通讯作者),AF Engn Univ, Equipment Management & Safety Engn Coll, Xian 710038, Shaanxi, Peoples R China. 地址: [ 1 ] Xijing Coll, Xian 710123, Shaanxi, Peoples R China [ 2 ] AF Engn Univ, Equipment Management & Safety Engn Coll, Xian 710038, Shaanxi, Peoples R China [ 3 ] AF Engn Univ, Coll Engn, Xian 710038, Shaanxi, Peoples R China 电子邮件地址: Ganxsheng123@163.com 出版商: ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS Web of Science 类别: Computer Science, Artificial Intelligence 研究方向: Computer Science IDS 号: 095HR ISSN: 0950-7051 |
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