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Fraud Website Detection using Data Mining

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

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

	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 = {},
	doi = {10.5120/ijca2016909590},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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  11. Data for legal websites,
  12. Data for Fraud websites,


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