Phishing Website Detection using Machine Learning
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, appearin...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2063/ http://eprints.intimal.edu.my/2063/2/604 http://eprints.intimal.edu.my/2063/3/joit2024_30b.pdf |
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| author | Padmini, Y Usha, Sree |
| author_facet | Padmini, Y Usha, Sree |
| author_sort | Padmini, Y |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:39Z |
| format | Article |
| id | intimal-2063 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:39Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20632025-07-11T03:10:21Z http://eprints.intimal.edu.my/2063/ Phishing Website Detection using Machine Learning Padmini, Y Usha, Sree QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) 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. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2063/2/604 text en cc_by_4 http://eprints.intimal.edu.my/2063/3/joit2024_30b.pdf 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 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Padmini, Y Usha, Sree Phishing Website Detection using Machine Learning |
| title | Phishing Website Detection using Machine Learning |
| title_full | Phishing Website Detection using Machine Learning |
| title_fullStr | Phishing Website Detection using Machine Learning |
| title_full_unstemmed | Phishing Website Detection using Machine Learning |
| title_short | Phishing Website Detection using Machine Learning |
| title_sort | phishing website detection using machine learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2063/ http://eprints.intimal.edu.my/2063/ http://eprints.intimal.edu.my/2063/2/604 http://eprints.intimal.edu.my/2063/3/joit2024_30b.pdf |