| 基于SVM主动学习算法的网络钓鱼检测系统 |
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| 引用本文： | 何高辉,邹福泰,谭大礼,王明政. 基于SVM主动学习算法的网络钓鱼检测系统[J]. 计算机工程, 2011, 37(19): 126-128. DOI: 10.3969/j.issn.1000-3428.2011.19.041 |
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| 作者姓名： | 何高辉 邹福泰 谭大礼 王明政 |
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| 作者单位： | 上海交通大学信息安全工程学院,上海,200240 |
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| 基金项目： | 国家自然科学基金资助项目(61071081); 上海市自然科学基金资助项目(09ZR1414900) |
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| 摘 要： | 针对钓鱼式网络攻击,从URL入手,对网址URL和Web页面内容综合特征进行识别、分类,实现网络钓鱼检测并保证检测的效率和精度.用支持向量机主动学习算法和适合小样本集的分类模型提高分类性能.实验结果证明,网络钓鱼检测系统能达到较高的检测精度.
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| 关 键 词： | 网络钓鱼 支持向量机 主动学习算法 分类器 敏感特征 |
| 收稿时间： | 2011-03-07 |
Phishing Detection System Based on SVM Active Learning Algorithm |
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| HE Gao-hui,ZOU Fu-tai,TAN Da-li,WANG Ming-zheng. Phishing Detection System Based on SVM Active Learning Algorithm[J]. Computer Engineering, 2011, 37(19): 126-128. DOI: 10.3969/j.issn.1000-3428.2011.19.041 |
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| Authors: | HE Gao-hui ZOU Fu-tai TAN Da-li WANG Ming-zheng |
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| Affiliation: | HE Gao-hui,ZOU Fu-tai,TAN Da-li,WANG Ming-zheng(School of Information Security Engineering,Shanghai Jiaotong University,Shanghai 200240,China) |
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| Abstract: | To detect and prevent various kinds of phishing attacks,there are many different preventive strategies and detective ideas.This paper takes the research of URL as the point of departure,describes how to detect phishing through the identification classification of the integrated features of URL and Web page content so as to ensure the efficiency and accuracy of detection.In the decision of the classification algorithm,it chooses Support Vector Machine(SVM) active learning algorithm which adapts to the small ... |
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| Keywords: | phishing Support Vector Machine(SVM) active learning algorithm classifier sensitive characteristic |
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