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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| Format: | Article |
| Language: | English |
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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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| _version_ | 1849417692144992256 |
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| author | Akshay Shankar Agrawal Sanketi Raut Andrina Dsouza Jimit Mehta Prajwal Naik |
| author_facet | Akshay Shankar Agrawal Sanketi Raut Andrina Dsouza Jimit Mehta Prajwal Naik |
| author_sort | Akshay Shankar Agrawal |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-8b7fd5703af842b8aaea82f2dd661934 |
| institution | Kabale University |
| issn | 2447-0228 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Institute of Technology and Education Galileo da Amazônia |
| record_format | Article |
| series | ITEGAM-JETIA |
| spelling | doaj-art-8b7fd5703af842b8aaea82f2dd6619342025-08-20T03:32:41ZengInstitute of Technology and Education Galileo da AmazôniaITEGAM-JETIA2447-02282025-06-01115310.5935/jetia.v11i53.1652Application of Machine Learning for Real-Time Phishing Attack DetectionAkshay Shankar Agrawal0Sanketi Raut1Andrina Dsouza2Jimit Mehta3Prajwal Naik4UNIVERSITY OF MUMBAIUniversity of Mumbai, Mumbai. India.Department of Information Technology, Universal College of Engineering, University of Mumbai, Vasai, India.Department of Information Technology, Universal College of Engineering, University of Mumbai, Vasai, India.Department of Information Technology, Universal College of Engineering, University of Mumbai, Vasai, India. 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. http://itegam-jetia.org/journal/index.php/jetia/article/view/1652 |
| spellingShingle | Akshay Shankar Agrawal Sanketi Raut Andrina Dsouza Jimit Mehta Prajwal Naik Application of Machine Learning for Real-Time Phishing Attack Detection ITEGAM-JETIA |
| title | Application of Machine Learning for Real-Time Phishing Attack Detection |
| title_full | Application of Machine Learning for Real-Time Phishing Attack Detection |
| title_fullStr | Application of Machine Learning for Real-Time Phishing Attack Detection |
| title_full_unstemmed | Application of Machine Learning for Real-Time Phishing Attack Detection |
| title_short | Application of Machine Learning for Real-Time Phishing Attack Detection |
| title_sort | application of machine learning for real time phishing attack detection |
| url | http://itegam-jetia.org/journal/index.php/jetia/article/view/1652 |
| work_keys_str_mv | AT akshayshankaragrawal applicationofmachinelearningforrealtimephishingattackdetection AT sanketiraut applicationofmachinelearningforrealtimephishingattackdetection AT andrinadsouza applicationofmachinelearningforrealtimephishingattackdetection AT jimitmehta applicationofmachinelearningforrealtimephishingattackdetection AT prajwalnaik applicationofmachinelearningforrealtimephishingattackdetection |