Call for Paper - July 2019 Edition
IJCA solicits original research papers for the July 2019 Edition. Last date of manuscript submission is June 20, 2019. Read More

Anti-Phishing based on Text Classification using Bayesian Approach

Print
PDF
IJCA Proceedings on International Conference on Advances in Science and Technology
© 2015 by IJCA Journal
ICAST 2014 - Number 3
Year of Publication: 2015
Authors:
Pankaj H. Gawale
D. R. Patil

Pankaj H Gawale and D R Patil. Article: Anti-Phishing based on Text Classification using Bayesian Approach. IJCA Proceedings on International Conference on Advances in Science and Technology ICAST 2014(3):19-22, February 2015. Full text available. BibTeX

@article{key:article,
	author = {Pankaj H. Gawale and D. R. Patil},
	title = {Article: Anti-Phishing based on Text Classification using Bayesian Approach},
	journal = {IJCA Proceedings on International Conference on Advances in Science and Technology},
	year = {2015},
	volume = {ICAST 2014},
	number = {3},
	pages = {19-22},
	month = {February},
	note = {Full text available}
}

Abstract

Phishing is an act of cracking by single person or group of persons to stolen the personal confidential information such as credit card detail, bank account detail, passwords etc. , from unknown sufferer for illegal activities. In this paper we have implemented the text classifier using Bayesian approach for phishing detection. Text classifier works on textual content for measuring the similarity between the real web page and untrustworthy web page. Stemming is used for simplicity of our model. For generating threshold we used probabilistic approach with large data set of homepage URLs. The experimental result gives phishing pages detection ratio is 98. 87% also for FAR is nearly equal to zero.

References

  • A. Emigh. (2005, Oct. ). Online Identity Theft: Phishing Technology, Chokepoints and Countermeasures. Radix Laboratories Inc. , Eau Claire, WI [Online]. Available: http://www. antiphishing. org/phisging- dsh-report. pdf
  • A. Y. Fu, W. Liu, and X. Deng, "Detecting phishing web pages with visual similarity assessment based on earth mover's distance (EMD)", IEEE Trans. Depend. Secure Comput. , vol. 3, no. 4, pp. 301–311, Oct. -Dec. 2006.
  • N. Chou, R. Ledesma, Y. Teraguchi, and D. Boneh, "Client-side defense against web-based identity theft", in Proc. 11th Annu. Netw. Distribut. Syst. Secur. Symp. , San Diego, CA, Feb. 2005, pp. 119–128.
  • Y. Zhang, S. Egelman, L. Cranor, and J. Hong, "Phinding phish: Evaluating anti-phishing tools", in Proc. 14th Annu. Netw. Distribut. Syst. Secur. Symp. , San Diego, CA, Feb. 2007, pp. 1–16.
  • W. Liu, N. Fang, X. Quan, B. Qiu, and G. Liu, "Discovering phishing target based on semantic link network", Future Generat. Comput. Syst. , vol. 26, no. 3, pp. 381–388, Mar. 2010.
  • Y. Zhang, J. Hong, and L. Cranor, "CANTINA: A content-based approach to detecting phishing web sites", in Proc. 16th Int. Conf. World Wide Web, Banff, AB, Canada, May 2007, pp. 639–648.
  • P. Likarish, E. Jung, D. Dunbar, T. E. Hansen, and J. P. Hourcade, "B-APT: Bayesian anti-phishing toolbar", in Proc. IEEE Int. Conf. Commun. , Beijing, China, May 2008, pp. 1745–1749.
  • W. Liu, X. Deng, G. Huang, and A. Y. Fu, "An antiphishing strategy based on visual similarity assessment", IEEE Internet Comput. , vol. 10, no. 2, pp. 58–65, Mar. –Apr. 2006.
  • M. Chandrasekaran, K. Narayanan, and S. Upadhyaya, "Phishing email detection based on structural properties", in Proc. 9th Annu. NYS Cyber Secur. Conf. , New York, Jun. 2006, pp. 2–8.
  • H. Zang , G. Liu, Tommy W. , S. Chow ,"Textual and visual content based anti-phishing : a Bayesian approach", IEEE Transaction of neural network, 1532- 1446, 2011.