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Antiphishing Model with URL & Image based Webpage Matching

is a form of online identity theft associated with both social engineering and technical subterfuge and a major threat to information security and personal privacy. Many anti- phishing solutions, such as content analysis and HTML code analysis, rely on this property to detect fake web pages. However, these techniques failed, as phishers are now composing phishing pages with non-analyzable elements, such as images and flash objects. This paper proposes a new phishing detection scheme based on an URL domain identity & webpage image matching. At first, it identifies the similar authorized URL, using divide rule approach and approximate string matching algorithm. For this similar URL and input URL, the IP addresses will be identified. If their IP addresses doesn't match with each other, then it could be phishing URL and phase-I phishing report will be generated. Then, this suspected URL's webpage snapshot will be treated as an image during phase-II. In phase-II, keypoints will be detected and their features will be extracted. These features will be extracted using CCH descriptor. Then, match this suspected image features with the features of authorized webpage. If this matching crosses threshold value, then this webpage is phishing one. At last, final phishing report will be generated. As the combined approach of URL domain identity and webpage image matching used, it performs better than other existing tools.