INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15
Phishing URL mainly target individuals and/or organization through social engineering attacks by exploiting the humans’ weaknesses in information security awareness . These URLs lure online users to access fake websites and harvest their confidential information , such as debit/credit card numbers and other sensitive information . In this work , we introduce a phishing detection technique based on URL lexical analysis and machine learning classifiers. This dataset was processed to generate 22 different features that were reduced further to a smaller set using different features reduction techniques. Random Forest, Gradient Boosting, Neural Network , Xgboost and Support Vector Machine (SVM) classifiers were all evaluated, and results show the superiority of SVMs, which achieved the highest accuracy in detecting the URLs with a rate of 99.89%. Our approach can be incorporated within add-on/middleware features in Internet browsers for alerting online users whenever they try to access a phishing website using only its URL .
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Cite Article:
"URL PHISHING DETECTION USING MACHINE LEARNING ", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1643 - 1645, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304261.pdf
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Publication Details:
Published Paper ID: IJSDR2304261
Registration ID:204814
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1643 - 1645
Publisher: IJSDR | www.ijsdr.org
ISSN Number: 2455-2631
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