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Detection of Fraudulent Websites

Mr.Murali. G, Aniruddh. S.S, Abhilash. G.K, Amar Chavan, Darshan J

Abstract


The frequency of phishing attacks through spoofed websites and using opens source tools like ngrok, metasploit payloads etc. have become very high. This paper deals with methods and techniques to counter these issues. This paper mainly deals with countering website phishing attacks through data mining algorithms and feature detection. The J48 algorithm used in this project is based on decision tree. And is very effective in predicting the phishing website. This paper also deals with precision of different classifiers JRIP, J48 and Naive base. The feature detection is not only the process used but also detects through the manual entry of data sets which we have explained in the following sections.


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References


Abdulghani Ali Ahmed, Nurul Amirah Abdulla., University Malaysia Pahang ,Real Time Detection of Phishing Websites IEEE November 2016.

Anti-Phishing Working Group Phishing, (2014). Anti-Phishing Working Group Phishing Trends Report. [Online] Available at: https://apwg.org/ [Accessed 30 Mar. 2015].

McAfee Labs Threats Report: February 2015. Retrieved from http://www.mcafee.com/us/resources/reports/rp- quar terly-threat-q4-2014.pdf.

Jagatic, T. N., Johnson, N. A., Jakobsson, M., & Menczer, F. (2007). Social phishing. Communications of the ACM, 50(10), 94-100.

Why HTTPS and SSL are not secure as you think (2014, March12) .

Zhang, Y., Hong, J. I., & Cranor, L. F. (2007, May). Cantina: a content-based approach to detecting phishing web sites. In Proceedings of the 16th international conference on World Wide Web (pp. 639-648). ACM.

Dunlop, M., Groat, S., & Shelly, D. (2010, May). Goldphish Using images for content-based phishing analysis. In Internet Monitoring and Protection (ICIMP), 2010 Fifth International Conference on (pp. 123-128). IEEE

Chou, N., Ledesma, R., Teraguchi, Y., & Mitchell, J. C. (2004, February). Client-Side Defense Against Web-BasedIdentity Theft. In NDSS.

Garera, S., Provos, N., Chew, M., & Rubin, A. D. (2007, November). A framework for detection and measurement of phishing attacks. In Proceedings of the 2007 ACM workshop on Recurring malcode (pp. 1-8). ACM.

Sheng, S., Wardman, B., Warner, G., Cranor, L. F., Hong, J., & Zhang, C. (2009). An empirical analysis of phishing blacklists.


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