基于特征选择与集成学习的钓鱼网站检测方法

针对目前大部分钓鱼网站检测方法存在检测准确率低、误判率高等问题，提出了一种基于特征选择与集成学习的钓鱼网站检测方法。该检测方法首先使用FSIGR算法进行特征选择，FSIGR算法结合过滤和封装模式的优点，从信息相关性和分类能力两个方面对特征进行综合度量，并采用前向递增后向递归剔除策略对特征进行选择，以分类精度作为评价指标对特征子集进行评价，从而获取最优特征子集；然后使用最优特征子集数据对随机森林分类算法模型进行训练。在UCI数据集上的实验表明，所提方法能够有效提高钓鱼网站检测的正确率，降低误判率，具有实际应用意义。

Method of phishing website detection based on feature selection and integrated learning

In view of the fact that most phishing websites detection methods have the problems of low detection accuracy and high false positive rate and other issues, this paper proposed a phishing website detection method based on feature selection and integrated learning. Firstly, the FSIGR algorithm was used to select feature. The FSIGR algorithm combined with the advantages of filter and wrapper modes. First, it carried out a comprehensive measurement of features from two aspects of information correlation and classification ability. Second, it used recursive elimination after increasing forward strategy to select the features, and used the classification accuracy as the evaluation index to measure and select the feature subset. Finally, it obtained the optimal feature subset. Then, the optimal feature subset data is used to train the random forest classification algorithm model.Experiments on the UCI dataset show that this method can improve the accuracy of phishing websites detection and reduce the false positive rate.