In order to effectively detect malicious phishing behaviors, a phishing detection method based on the uniform resource locator （URL） features is proposed. First, the method compares the phishing URLs with legal ones to extract the features of phishing URLs. Then a machine learning algorithm is applied to obtain the URL classification model from the sample data set training. In order to adapt to the change of a phishing URL, the classification model should be constantly updated according to the new samples. So, an incremental learning algorithm based on the feedback of the original sample data set is designed. The experiments verify that the combination of the URL features extracted in this paper and the support vector machine （SVM） classification algorithm can achieve a high phishing detection accuracy, and the incremental learning algorithm is also effective.
A phishing detection system, which comprises client-side filtering plug-in, analysis center and protected sites, is proposed. An image-based similarity detection algorithm is conceived to calculate the similarity of two web pages. The web pages are first converted into images, and then divided into sub-images with iterated dividing and shrinking. After that, the attributes of sub-images including color histograms, gray histograms and size parameters are computed to construct the attributed relational graph（ARG）of each page. In order to match two ARGs, the inner earth mover＇s distances（EMD）between every two nodes coming from each ARG respectively are first computed, and then the similarity of web pages by the outer EMD between two ARGs is worked out to detect phishing web pages. The experimental results show that the proposed architecture and algorithm has good robustness along with scalability, and can effectively detect phishing.
