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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH
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ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016
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Issue: April 2024

Volume 9 | Issue 4

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Paper Title:	Phishing websites feature classification using machine learning
Authors Name:	Sai teja Erakala , Jyoshna rani Deepala , Charitha kallimakula , Venkata Ashok Reddy Tatiparthi
Unique Id:	IJSDR2304275
Published In:	Volume 8 Issue 4, April-2023
Abstract:	Phishing websites that assume to attain sensitive statistics from victims, redirecting them to a fake website that appears very similar to a valid one, is some other sort of on line crook hobby and of precise problem in many regions, consisting of e-government. Mixed and wholesale. The detection of a hacked site is honestly indistinct and complex problem with many components and criteria that aren't solid. Because of this, and also the paradox in organizing sites because of the intelligent structures that programmers use, a few proactive strategies may be beneficial and effective tools that may be used, along with neural structures and metalworking techniques. Phishing site popularity mechanism. We used Random Forest (RF), one of the numerous kinds of device gaining knowledge of algorithms used to detect phishing pages. Finally, we measured and in comparison the overall performance of the classifier in terms of accuracy.
Keywords:	Logistic Regression, Support Vector Machine, Random Forest Classification, Machine Learning
Cite Article:	"Phishing websites feature classification using machine learning", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1757 - 1763, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304275.pdf
Downloads:	000337211
Publication Details:	Published Paper ID: IJSDR2304275 Registration ID:205551 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: 1757 - 1763 Publisher: IJSDR \| www.ijsdr.org ISSN Number: 2455-2631

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