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Received:December 26, 2014 Revised:February 11, 2015
Received:December 26, 2014 Revised:February 11, 2015
中文摘要: 针对日益严重的网络钓鱼攻击, 提出机器学习的方法进行钓鱼网站的检测和判断. 首先, 根据URL提取敏感特征, 然后, 采用AdaBoost算法进行训练出分类器, 再用训练好的分类器对未知URL检测识别. 最后, 针对非平衡代价问题, 采用了改进后的AdaBoost算法--AdaCostBoost, 加入代价因子的计算. 实验结果表明, 文中提出的网络钓鱼检测方法, 具有较优的检测性能.
中文关键词: 网络钓鱼 敏感特征 AdaCostBoost 分类器 代价因子
Abstract:For increasing serious phishing attacks, machine-learning method is proposed to detect phishing webs. Firstly, sensitive features are extracted from the URL, then, using AdaBoost algorithm to get the trained classifier, and then the classifier is used to detect unknown URLs. Finally, considering of non-equilibrium problems of AdaBoost, the paper puts forward the improved learning algorithm called AdaCostBoost, which contains computation of cost factors. According to the experiment result, the proposed phishing detection method has better detection performance.
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基金项目:国家自然科学基金(11062002)
引用文本：
曾传璜,李思强,张小红.基于AdaCostBoost算法的网络钓鱼检测.计算机系统应用,2015,24(9):129-133
ZENG Chuan-Huang,LI Si-Qiang,ZHANG Xiao-Hong.Phishing Detection System Based on AdaCostBoost Algorithm.COMPUTER SYSTEMS APPLICATIONS,2015,24(9):129-133
曾传璜,李思强,张小红.基于AdaCostBoost算法的网络钓鱼检测.计算机系统应用,2015,24(9):129-133
ZENG Chuan-Huang,LI Si-Qiang,ZHANG Xiao-Hong.Phishing Detection System Based on AdaCostBoost Algorithm.COMPUTER SYSTEMS APPLICATIONS,2015,24(9):129-133
