Visual Similarity Based Anti-phishing with the Combination of Local and Global Features

Abstract

Phishing uses a fake Web page to steal personal sensitive information such as credit card numbers and passwords. Generally, the fake Web page is visually similar to the legitimate target Web page. The phishers can obtain financial benefits through these information. Anti-phishing is very important for a variety of applications such as phishing attacks, online transaction security, and user privacy protection. In this paper, we propose a novel and effective visual similarity based phishing detection approach that compares the snapshot image pair of the suspected Web page and the protected Web page. The proposed approach is based on the key insight that both the local and the global features of the Web page image can be used to represent the visual characteristics of the Web page together. This approach is purely on the image level, and thus can effectively deal with the non-text phishing tricks including images or Flashes objects in the HTML contents. For the local feature, the existence of the target logo is detected. For the global feature, the similarity of the visible part of the Web page is considered. We implemented and evaluated the proposed approach on a large scale dataset consisting of 2,129 real world phishing Web pages and 1,367 irrelevant legitimate Web pages. The experimental results show that the proposed approach can achieve over 90.00% true positive rate and 97.00% true negative rate. Our approach has been applied in the anti-phishing project of a major Internet Service Provider and gives a periodical reports to the potential users.