Intelligent Detection Method Of Phishing Webpages Using Deep Learning

Phishing attacks, which steal users'account information by fake websites, have become a serious problem on the Internet. Phishing websites are forged web pages that are created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying Phishing websites is really a complex and dynamic problem involving many factors and criteria. In this paper, we propose a phishing detection mechanism based on Deep Belief Networks (DBN), which the features of web image and DOM objects/properties of web are taken in consideration, and the Deep Belief Networks is used to detect and classify phishing web pages.Firstly, the features of web image are extracted for complementing the disadvantage of phishing detection only based on document object model (DOM). Then according to the results of web image segmentation, the features of web image that include boundary shape, gray histogram, color histogram, and spatial relationship between subgraphs. According to DOM objects, the features of web sensitive information are examined, which include URL, form, SSL certificate and so on. Secondly, as the data formats that comprise web characteristic vector are different and exist redundant, the web classifier needs more time to handle characteristic data. In order to solve the problem, this paper introduced the efficiency Isomap to normalize and reduce dimension of characteristic vectors, this make the data to fit for the phishing web classifier input requirements. These futures of web page are used to assess phishing page by the DBN (Deep Belief Networks). At last, we developed an phishing web detection system based on Deep Belief Networks algorithm, in this paper, a detailed introduction to the phishing web detection system was displayed, including the design of system, database design, and the interfaces of system functions.