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Issue No.03 - May/June (2009 vol.13)
pp: 56-63
Jau-Yuan Chen , Columbia University
Chun-Rong Huang , Institute of Information Science at Academia Sinica
Chu-Song Chen , Institute of Information Science and the Research Center for Information Technology Innovation at Academia Sinica
ABSTRACT
Phishing is a form of online identity theft associated with both social engineering and technical subterfuge and is a major threat to information security and personal privacy. Here, the authors present an effective image-based antiphishing scheme based on discriminative keypoint features in Web pages. Their invariant content descriptor, the Contrast Context Histogram (CCH), computes the similarity degree between suspicious and authentic pages. The results show that the proposed scheme achieves high accuracy and low error rates.
INDEX TERMS
antiphishing, Contrast Context Histogram, image matching, local content descriptor, visual similarity, phishing
CITATION
Jau-Yuan Chen, Chun-Rong Huang, Chu-Song Chen, "Fighting Phishing with Discriminative Keypoint Features", IEEE Internet Computing, vol.13, no. 3, pp. 56-63, May/June 2009, doi:10.1109/MIC.2009.59
REFERENCES
1. "APWG Phishing Trends Reports," The Anti-Phishing Working Group, www.antiphishing.orgphishReports Archive.html .
2. "Gartner Survey Shows Phishing Attacks Escalated in 2007, More than $3 Billion Lost to These Attacks," Gartner, 2007; www.gartner.com/itpage.jsp?id=565125.
3. C.-R. Huang, C.-S. Chen, and P.-C. Chung, "Contrast Context Histogram —A Discriminating Local Descriptor for Image Matching," Proc. Int'l Conf. Pattern Recognition (ICPR 06), IEEE CS Press, 2006, pp. 53–56.
4. C.-R. Huang, C.-S. Chen, and P.-C. Chung, "Contrast Context 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.
5. K. Mikolajczyk and C. Schmid, "Indexing Based on Scale Invariant Interest Points," Proc. Int'l Conf. Computer Vision, vol. 1, IEEE Press, 2001, pp. 525–531.
6. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000.
7. "Phishing Statistics," Secure Computing, 2007, www.ciphertrust.com/resources/statistics phishing.php.
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