Along with deployment of internet, saving financial and sensitive information becomes more inconvenient. One of the problems faced today is growing number of phishing websites. Phishing websites are fake webpage shaped and used by phishers to copy the web pages of legitimate websites which results in lack of faith in internet based services and causes financial loss to the internet users. So it has become crucial to search for useful solution applicable for such phishing websites. Therefore, establishing useful solution for mitigating phishing websites is essential to reduce the incident of being victimized by phishing attack. This research paper employs approach that uses fuzzy logic with classifiers like SVM, NMC and Gaussian. Fuzzy based detection system provides effective aid in detecting phishing websites. It successfully resulted in low false positive and high true positive for classifying phishing websites.

Phishing;SVM;Gaussian; Fuzzy Logic;Feature Collection

[1]Aburrous, M., Hossain, M.A.; Thabatah, F.; and Dahal, K. (2008): Intelligent phishing website detection system using fuzzy techniques. Information and Communication Technologies: From Theory to Applications. ICTTA. April 7-11, 2008, pp.1-6.
[2] Cyren report accessed june 2015.
[3] The Economic Times news accessed july 2015.
[4] Andrew H.Sung, Ram Basenet, Srinivas Mukkamala.(2008) “Detection of Phishing Attacks: A machine Learning Approach”. InB. Parsad, editor, Springer ,Soft Computing Applications in Industry, Studies in Fuzziness and Soft Computing, volume 226, pages 373–383.
[5] Anh Le,Athina Markopoulou,Michalis Faloutsos:PhishDef: URL Names Say it All.

[6] Santhana Lakshmi V, Vijaya MS, (2012) : Efficient prediction of phishing websites using supervised learning algorithms, Procedia Engineering 30 pp.798 – 805.
[7] Jiang, Hansi; Zhang, Dongsong; and Yan, Zhijun (2013):A Classification Model for Detection of Chinese Phishing E-Business Websites, PACIS Proceedings, Paper 152.
[8] Rami M. Mohammad, Fadi Thabtah.(2012): An assessment of features related to phishing websites using an automated technique. Internet Technology and Secured Transactions, pp-492-497.
[9] Adi Sutanto, Jui-Lin Lai, Muhammad Khurram Khan, Mingxing He, Pingzhi, Rong-Jian Chen,, Ray-Shine Run, Shi-Jinn Horng,(2011):An efficient phishing webpage detector. Expert Systems with Applications,38, pp.12018–12027.
[10] Comparison of generalization properties of statistical and neural classifiers available at< http://www.mif.vu.lt/~valdas/PhD/TEXT/CHAPTER4.pdf>.
[11] PhishTank. [Online]. Available from: http://www.phishtank.com/.
[12] Antiphishing Working Group, Available from: www.antiphishing.org/reports/apwg-reports.
[13] Zhang, Y., Hong, J., and Cranor, L. (2007). CANTINA: A Content-Based Approach to Detecting Phishing Websites. Proceedings of the 16th International Conference on World Wide Web. pp. 639-648.
[14] Rashmi Gupta, Nivit Gill. (2013): Token Based Security for Prevention of Phishing Attack at Client Side. International Journal of Emerging Technologies in Computational and Applied Sciences, pp.184-189.
[15] Ram B. Basnet, Andrew H. Sung, Quingzhong Liu.(2014): Learning to detect phishing urls. IJRET: International Journal of Research in Engineering and Technology, pp.11-14.
[16] Mingxing He, Horng SJ, Fan P, Khan MK, Run RS, Lai JL, Chen RJ.(2011) “An efficient phishing webpage detector. Expert Systems with Applications”, An International Journal. pp. 12018-12027.