Phishing 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.

1. The Anti-Phishing Working Group,APWG Phishing Trends-Reports, www.antiphishing.org/phishReports Archive.html
2. Jason Milletary, Technical Trends in Phishing Attacks, Carnegie Mellon University,2005
3. Sumit Siddharth, Anti Spamming Techniques.pdf.
4. P. Robichaux and D.L. Ganger, Gone Phishing: Evaluating Antiphishing Tools for Windows, 3Sharp Project Report, Sept. 2006; www.3sharp.com/ projects/antiphishing/.
5. C. Ludl et al., On the Effectiveness of Techniques to Detect Phishing Sites, Proc. Detection of Intrusions and Malware, and Vulnerability Assessment, LNCS 4579, Springer, 2007, pp. 20–39
6. W. Liu et al., An Antiphishing Strategy Based on Visual Similarity Assessment, IEEE Internet Computing, vol. 10, no. 2, 2006, pp. 58–65
7. L. Wenyin et al., Detection of Phishing Webpages Based on Visual Similarity,Proc. World Wide Web Conf. (special interest tracks and posters), A. Ellis and T. Hagino, eds., ACM Press, 2005, pp. 1060–1061.
8. IEEE 2009 Paper on Fighting Phishing with Discriminative Keypoint Features by K. J. Lin, Yan Wang.
9. J. Han and M. Kamber,Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
10. C.-R. Huang, C.-S. Chen, and P.-4. C. Chung, ContrastContext Histogram — An Efficient Discriminating Local Descriptor for Object Recognition and Image Matching,Pattern Recognition, vol. 41, no. 10, 2008, pp.3071–3077; http://imp.iis.sinica.edu.tw/CCH/CCH.htm.

Phishing networking keypoints string matching image Matching