INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
"DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g349-g352, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403641.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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