基于决策树的钓鱼网页的识别方法

Recognition Method of Fishing Web Pages Based on Decision Tree

现如今许多不法分子利用钓鱼网站盗取用户的个人信息,窃取用户的财产,对用户造成巨大损失。因此该文通过使用决策树学习算法,提取其中的关键词,分析并建立钓鱼网站特征模型,对未知网站进行判别。CART是一种决策树算法,但CART决策树的多数表决法会屏蔽小类数据类型的影响,因此该文根据这点对CART决策树进行改进,引入代价函数,不断地利用迭代和最小均方误差调整特征的权重增加惩罚。实验结果表明,改进后的决策树在对未知网站进行分析,成功地降低了负样本的错误率,提升了识别率。

Now many criminals use phishing sites to steal the user's personal information, steal the user's property, causing huge losses to the user. Therefore, this paper uses the decision tree learning algorithm to extract the keywords, analyze and establish the phishing website feature model, and judge the unknown website. CART is a decision tree algorithm, but the majority voting method of CART decision tree will shield the influence of small class data type. Therefore, this paper improves the CART decision tree according to this point, introduces the cost function, and makes use of iteration and minimum mean square error Adjust the weight of the feature to increase the penalty. The experimental results show that the improved decision tree has successfully reduced the error rate of negative samples and improved the recognition rate in the analysis of unknown websites.