Paper Title
DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING
Article Identifiers
Authors
V.Ramana Murthy , D.Neeraja , D.Rohit SivaReddy , B.Rakesh , V.Swathika,B.Uday Kiran
Keywords
Logistic Regression,Multinomial Naïve Bayes,XG Boost.
Abstract
Criminals seeking sensitive information construct illegal clones of actual websites and e-mail accounts. The e-mail will be made up of real firm logos and slogans. When a user clicks on a link provided by these hackers, the hackers gain access to all of the user's private information, including bank account information, personal login passwords, and images. Random Forest and Decision Tree algorithms are heavily employed in present systems, and their accuracy has to be enhanced. The existing models have low latency. Existing systems do not have a specific user interface. In the current system, different algorithms are not compared. Consumers are led to a faked website that appears to be from the authentic company when the e-mails or the links provided are opened. The models are used to detect phishing Websites based on URL significance features, as well as to find and implement the optimal machine learning model. Logistic Regression, Multinomial Naive Bayes, and XG Boost are the machine learning methods that are compared. The Logistic Regression algorithm outperforms the other two.
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How To Cite
"DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g349-g352, March-2024, Available :https://ijnrd.org/papers/IJNRD2403641.pdf
Issue
Volume 9 Issue 3, March-2024
Pages : g349-g352
Other Publication Details
Paper Reg. ID: IJNRD_216714
Published Paper Id: IJNRD2403641
Downloads: 000121128
Research Area: Computer Science & TechnologyÂ
Country: Visakhapatnam , Andhra Pradesh, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2403641.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2403641
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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition


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