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Analysis of Efficient Classification Algorithm for Detection of Phishing Site
Meenu Shukla1 , Sanjiv Sharma2
- Department of CSE/IT, Madhav Institute of Technology and Science, Gwalior, India.
- Department of CSE/IT, Madhav Institute of Technology and Science, Gwalior, India.
Correspondence should be addressed to: shuklameenu03@gmail.com.
Section:Research Paper, Product Type: Isroset-Journal
Vol.5 ,
Issue.3 , pp.136-141, Jun-2017
Online published on Jun 30, 2017
Copyright © Meenu Shukla, Sanjiv Sharma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Meenu Shukla, Sanjiv Sharma, âAnalysis of Efficient Classification Algorithm for Detection of Phishing Site,â International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.136-141, 2017.
MLA Style Citation: Meenu Shukla, Sanjiv Sharma "Analysis of Efficient Classification Algorithm for Detection of Phishing Site." International Journal of Scientific Research in Computer Science and Engineering 5.3 (2017): 136-141.
APA Style Citation: Meenu Shukla, Sanjiv Sharma, (2017). Analysis of Efficient Classification Algorithm for Detection of Phishing Site. International Journal of Scientific Research in Computer Science and Engineering, 5(3), 136-141.
BibTex Style Citation:
@article{Shukla_2017,
author = {Meenu Shukla, Sanjiv Sharma},
title = {Analysis of Efficient Classification Algorithm for Detection of Phishing Site},
journal = {International Journal of Scientific Research in Computer Science and Engineering},
issue_date = {6 2017},
volume = {5},
Issue = {3},
month = {6},
year = {2017},
issn = {2347-2693},
pages = {136-141},
url = {https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=410},
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
UR - https://www.isroset.org/journal/IJSRCSE/full_paper_view.php?paper_id=410
TI - Analysis of Efficient Classification Algorithm for Detection of Phishing Site
T2 - International Journal of Scientific Research in Computer Science and Engineering
AU - Meenu Shukla, Sanjiv Sharma
PY - 2017
DA - 2017/06/30
PB - IJCSE, Indore, INDIA
SP - 136-141
IS - 3
VL - 5
SN - 2347-2693
ER -
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.
Key-Words / Index Term :
Phishing sites, URL based features, Web Source Based Features, Machine learning, and Random Forest
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