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Fraud Website Detection using Data Mining

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Urvashi Prajapati, Neha Sangal, Deepti Patole
10.5120/ijca2016909590

Neha Sangal Urvashi Prajapati and Deepti Patole. Fraud Website Detection using Data Mining. International Journal of Computer Applications 141(3):40-44, May 2016. BibTeX

@article{10.5120/ijca2016909590,
	author = {Urvashi Prajapati, Neha Sangal and Deepti Patole},
	title = {Fraud Website Detection using Data Mining},
	journal = {International Journal of Computer Applications},
	issue_date = {May 2016},
	volume = {141},
	number = {3},
	month = {May},
	year = {2016},
	issn = {0975-8887},
	pages = {40-44},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume141/number3/24768-2016909590},
	doi = {10.5120/ijca2016909590},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Phishing attack is used to steal confidential information of user. Fraud websites appear similar to genuine websites with the logo and graphics of trusted website. Fraud Website Detection application aims to detect fraud websites using data mining techniques. This project provides intelligent solution to phishing attack. W3C standard defines characteristics which can be used to distinguish fraud and legal website. This application extracts some characteristics from URL and source code of a website. These features are used for classification. RIPPER algorithm is used to classify the websites. After classifying the websites, the application sends notification email to the administrator using WHOIS protocol. The administrator may block the fraud website after verification.

References

  1. Peter Stavroulakis, Mark Stamp,“Handbook of Information and Communication Security”,Springer.
  2. Phishing statistics, https://docs.apwg.org/reports/apwg_trends_report_q4_2014.pdf
  3. M. Dunlop, S. Groat, and D. Shelly," GoldPhish: Using Images for Content-Based Phishing Analysis", in the Fifth International Conference on Internet Monitoring and Protection, 2010.
  4. JRip algorithm pseudocode, http://weka.sourceforge.net/doc.dev/weka/classifiers/rules/JRip.html
  5. Robert Stahlbock, Sven F. Crone, Stefan Lessmann ,“Data Mining: Special Issue in Annals of Information Systems”,Springer.
  6. Zhongyu Lu, “Information Retrieval Methods for Multidisciplinary Applications”, Information Science Reference.
  7. Mohammad, R., Thabtah, F., & McCluskey, L. (2012). ― An assessment of features related to phishing websites using an automated technique‖. In The 7th international conference for internet technology and secured transactions(ICITST-2012). London: ICITST.
  8. Omar Abdullah Batarfi Mona Ghotaish Alkhozae, “Phishing websites detection based on phishing characteristics in the webpage source code,” International Journal of Information and Communication Technology Research, October 2011.
  9. Garth O. Bruen ,“WHOIS Running the Internet: Protocol, Policy, and Privacy”,Wiley.
  10. Ihab Shraim, Laura Mather, Patrick Cain, Rod Rasmussen, “Advisory on Utilization of Whois Data For Phishing Site Take Down ”, APWG Internet Protocol Committee,March 2008.
  11. Data for legal websites, http://www.dmoz.org/
  12. Data for Fraud websites, https://www.phishtank.com/

Keywords

Phishing, JRip, RIPPER, Fraud website, source code, data mining, WHOIS