Analysis of Efficient Classification Algorithm for Detection of Phishing Site

Authors

  • Meenu Shukla Department of CSE/IT, Madhav Institute of Technology and Science, RGPV, Gwalior, India
  • Sanjiv Sharma Department of CSE/IT, Madhav Institute of Technology and Science, RGPV, Gwalior, India

Keywords:

Phishing sites, URL based features, Web Source Based Features, Machine learning, Random Forest

Abstract

Phishing is mainly related to steal the confidential information and personal data of web users by making duplicate of the original one in which the content and images are almost similar to the legitimate website with small changes. Other method of phishing is to make changes in the URL that is approximately similar to legitimate website. Here we have discussed the different methods for phishing detection and some of the disadvantages of them. In this paper, we proposed phishing detection on the basis of web source and uses uniform resource locator (URL) features. We identified the features that contained in the phishing URLs. The technique is evaluated with a dataset of phishing URLs and White List URLs. The results evaluated shows that with this technique we are able to detect more phishing sites.

 

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Published

2017-06-30

How to Cite

[1]
M. Shukla and S. Sharma, “Analysis of Efficient Classification Algorithm for Detection of Phishing Site”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 5, no. 3, pp. 136–141, Jun. 2017.

Issue

Vol. 5 No. 3 (2017): June

Section

Research Article

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