Application of Machine Learning for Real-Time Phishing Attack Detection
Over the years, the Internet has been exploited to carry out a range of cyber attacks, with phishing being the most prominent one. Increasingly sophisticated techniques of phishing have threatened the security of many Internet-based systems. To be able to detect suspicious websites is a potential f...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Technology and Education Galileo da Amazônia
2025-06-01
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| Series: | ITEGAM-JETIA |
| Online Access: | http://itegam-jetia.org/journal/index.php/jetia/article/view/1652 |
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| Summary: | Over the years, the Internet has been exploited to carry out a range of cyber attacks, with
phishing being the most prominent one. Increasingly sophisticated techniques of phishing
have threatened the security of many Internet-based systems. To be able to detect suspicious
websites is a potential first step in reducing the amount of phishing attacks occurring daily.
This paper outlines the development and implementation of a platform to detect phishing
websites. It highlights the pressing need for early detection of possible phishing attacks to
prevent data theft, frauds, etc. The system uses machine learning algorithms to distinguish
legitimate websites from phishing websites and generate a prediction to be used for the
platform. A user interface is implemented to have two parts. The first part includes a text
field for entering a URL, which the ML model processes to give a prediction that gets
displayed to the user. Another module gathers URLs as they arrive from an API and scans
them for potentially suspicious websites. The final ML model, a Random Forest classifier
with 27 estimators, had an accuracy of 96.12% and F1 score of 95.94%. Future
enhancements and research directions are also discussed for further development of the
system.
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| ISSN: | 2447-0228 |