DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING

Registration ID: IJNRD_216714

Published ID: IJNRD2403641

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V.Ramana Murthy, D.Neeraja, D.Rohit SivaReddy, B.Rakesh , V.Swathika,B.Uday Kiran

Logistic Regression,Multinomial Naïve Bayes,XG Boost.

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.

Published Paper   E-Certificate

V.Ramana Murthy, D.Neeraja, D.Rohit SivaReddy, B.Rakesh , & V.Swathika,B.Uday Kiran (March-2024). DETECTION OF PHISHING WEBSITES USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), g349-g352. https://ijnrd.org/papers/IJNRD2403641.pdf

Volume 9 Issue 3, March-2024

Pages : g349-g352

Paper Reg. ID: IJNRD_216714

Published Paper Id: IJNRD2403641

Downloads: 000121994

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Visakhapatnam , Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403641.pdf

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