Phishing Website Detection using Machine Learning

Padmini, Y and Usha, Sree (2024) Phishing Website Detection using Machine Learning. Journal of Innovation and Technology, 2024 (30). pp. 1-7. ISSN 2805-5179

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Abstract

Phishing attacks, a prevalent and significant form of cybercrime, involve attackers masquerading as reputable entities to deceive individuals into revealing sensitive details such as usernames, passwords, and credit card information. Deceptive websites are commonly used in these attacks, appearing legitimate and underscoring the need for individuals and organizations to heighten their awareness and implement stronger and more advanced detection techniques. By luring sensitive information through deceptive websites, phishing attacks represent a serious cybersecurity threat. In this research, the effectiveness of machine learning algorithms, specifically the Gradient Boosting Classifier, in identifying phishing websites to enhance accuracy and response time is being assessed.

Item Type:	Article
Uncontrolled Keywords:	Phishing attacks, Machine Learning, Cybersecurity, Gradient Boosting, Websites
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	28 Nov 2024 02:32
Last Modified:	28 Nov 2024 02:32
URI:	http://eprints.intimal.edu.my/id/eprint/2063

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