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Prevention of Phishing Attacks Based on Discriminative Key Point Features of WebPages
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International Journal of Computer Science and Security (IJCSS)
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Volume:  6    Issue:  1
Pages:  
Publication Date:   February 2012
ISSN (Online): 1985-1553
Pages 
1 - 18
Author(s)  
Mallikka Rajalingam - Malaysia
Salah Ali Alomari - Malaysia
Putra Sumari - Malaysia
 
Published Date   
21-02-2012 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Image Clustering and Retrieval, Anti-Phishing mechanism, Digital Image Processing, Security, CCH 
 
 
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Phishing is the combination of social engineering and technical exploits designed to convince a victim to provide personal information, usually for the monetary gain of the attacker (Phisher). Attempts to stop phishing by preventing a user from interacting with a malicious web site have shown to be ineffective. In this paper, present an effective image-based anti-phishing scheme based on discriminative key point features in WebPages. We use an invariant content descriptor, the Contrast Context Histogram (CCH), to compute the similarity degree between suspicious pages and authentic pages. To determine whether two images are similar, a common approach involves extracting a vector of salient features from each image, and computing the distance between the vectors, which is taken as the degree of visual difference between the two images. The results show that the proposed scheme achieves high accuracy and low error rates. 
 
 
 
1 A. Kannan, V. Mohan and N. Anbazhagan. ”Image Clustering and Retrieval using Image Mining Techniques”. IEEE International Conference on Computational Intelligence and Computing Research, vol.2, 2010
2 SOPHOS 2005, http://www.sophos.com/whitepapers/sophos-phishing-wpuk.pdf, accessed April 2011
3 M. Jakobsson, and S. Myers: ‘Phishing and Countermeasures: Understanding the Increasing Problem of Electronic Identity Theft’ Wiley, 2007
4 W. Burger and M. Burge. “Digital image processing: an algorithmic introduction using Java”. Springer, Pages: 240-250, 2008
5 S.R. Kodituwakku et al. ”Comparison of Color Features for Image Retrieval”. Indian Journal of Computer Science and Engineering, vol.1, no.3, pp.207-211, 2004
6 APWG, http://www.antiphishing.org/index.html, accessed March 2011
7 Wikipedia, http://en.wikipedia.org/wiki/Phishing, accessed April 2011
8 Webopedia, http://www.webopedia.com/TERM/P/phishing.html, accessed April 2011
9 M. Aburrous, M.A.Hossain, Keshav Dahal and Fadi Thabtah. “Experimental Case Studies for Investigating E-Business Phishing Techniques and Attack Strategies”. Springer Science, Cong Comput 2010, vol.2, No.242-253, April 2010
10 APWG. http://www.apwg.org/reports/APWG_GlobalPhishingSurvey_1H2009.pdf , accessed 8 August 2009
11 Juan Chen and Chuanxiong. “Online Detection and Prevention of Phishing Attacks”. IEEE Communications and Networking, NSFC, 2005
12 M. Chandrasekaran, K Narayanan and S Upadhaya,”PHONEY:Mimicking User Response to Detect Phishing Attacks”, To appear at TSPUC Workshop, affiliated with IEEE WoWMoM, 2005
13 K. Chen, C. Huang and C. Chen. “Fighting Fishing With Discriminative Keypoint Features”. IEEE INTERNET COMPUTING, 2009
14 K. Plossl, H. Federrath and T. Nowey. “Protection Mechanisms Against Phishing Attacks”. Proc, 2nd Intl.Conf. on TrusBus 05, LNCS 3592, Springer-Verlag, 2005
15 M. Wu, R.C.Miller, S.L.Garfinkel, “Do security toolbars actually prevents phishing attacks?”, in CHI (to appear), 2006. [online]. Available: http://www.simson.net/ref/2006/CHI-securitytoolbar- final.pdf
16 S. Kierkegaard, “Swallowing the bait, hook, line and sinker: Phishing and Pharming and now rat-ting!”, in Managing Information Services in Financial Services H.R. Roa, M. Gupta, S. J. Upadhaya, Eds.USA:IGI publishing, 2008, pp.241-253.
17 N.P. Singh. “Online Frauds in Banks with Phishing”. Journal of Internet Banking and Commerce, vol.12, 2007
18 Phishtank. 2008 http://www.phishtank.com/phish_archive.php, accessed 14 November 2008
19 A. Abbasi and H. Chen. “A comparison of fraud cues and classification methods for fake escrow website detection”. Springer, Inf Technol March, 2009
20 R. Kanthety and S. Saradhi. “Prevention of Phishing Attacks using Link-Guard Algorithm”. International Journal of Computer Science Issues (IJCSI). vol. 7, no. 2, suppl.4, 31p.March 2010
21 A. Martin, Na.Ba.Anutthamaa, M. Sathyavathy, Marie Manjari Saint Francois and Dr. Prasanna Venkatesan. “A Framework for Predicting Phishing Websites Using Neural Networks”. International Journal of Computer Science Issues (IJCSI). vol. 8, Issue 2, March 2011
22 Bryan Parno, Cynthia Kuo, and Adrian Perrig. “Phoolproof of Phishing Prevention”. Financial Cryptography and Data Security, Springer, 2006
23 Total Number of Fraud Complaints & amount paid. 2003, http://www.consumer.gov/sentinel/states03/fraud_complaint_trends.pdf.
24 Tom Jagatic, Natheniel Johnson, Markus Jakobsson, and Filippo Menczer. “Social Phishing”. Communications of ACM, 2005
25 Thomas J. Holt and Danielle C. Graves. “A Qualitative Analysis of Advance Fee Fraud E-mail Schemes”. International journal of Cyber Criminology, vol.1, issue.1, 2006
26 http://mybank.com/ebanking]
 
 
 
 
 
 
 
 
Mallikka Rajalingam : Colleagues
Salah Ali Alomari : Colleagues
Putra Sumari : Colleagues  
 
 
 
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