A Deep Learning-Based Framework for Phishing Website Detection
Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9661323/ |
| _version_ | 1818943460884873216 |
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| author | Lizhen Tang Qusay H. Mahmoud |
| author_facet | Lizhen Tang Qusay H. Mahmoud |
| author_sort | Lizhen Tang |
| collection | DOAJ |
| description | Phishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third-party services, which will cause instability and time-consuming issues in the prediction service. In this paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets. From the experimental results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of the proposed solution. |
| first_indexed | 2024-12-20T07:27:41Z |
| format | Article |
| id | doaj.art-580cef17e80649cc9234478fcc77cd40 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T07:27:41Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-580cef17e80649cc9234478fcc77cd402022-12-21T19:48:29ZengIEEEIEEE Access2169-35362022-01-01101509152110.1109/ACCESS.2021.31376369661323A Deep Learning-Based Framework for Phishing Website DetectionLizhen Tang0Qusay H. Mahmoud1https://orcid.org/0000-0003-0472-5757Department of Electrical, Computer, and Software Engineering, Ontario Tech University, Oshawa, ON, CanadaDepartment of Electrical, Computer, and Software Engineering, Ontario Tech University, Oshawa, ON, CanadaPhishing attackers spread phishing links through e-mail, text messages, and social media platforms. They use social engineering skills to trick users into visiting phishing websites and entering crucial personal information. In the end, the stolen personal information is used to defraud the trust of regular websites or financial institutions to obtain illegal benefits. With the development and applications of machine learning technology, many machine learning-based solutions for detecting phishing have been proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on third-party services, which will cause instability and time-consuming issues in the prediction service. In this paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time when the user visits a web page and gives a warning message. The real-time prediction service combines multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including whitelist filtering, blacklist interception, and machine learning (ML) prediction. In the ML prediction module, we compared multiple machine learning models using several datasets. From the experimental results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of the proposed solution.https://ieeexplore.ieee.org/document/9661323/Phishing detectionmachine learningdeep learningRNN-GRUweb browser extension |
| spellingShingle | Lizhen Tang Qusay H. Mahmoud A Deep Learning-Based Framework for Phishing Website Detection IEEE Access Phishing detection machine learning deep learning RNN-GRU web browser extension |
| title | A Deep Learning-Based Framework for Phishing Website Detection |
| title_full | A Deep Learning-Based Framework for Phishing Website Detection |
| title_fullStr | A Deep Learning-Based Framework for Phishing Website Detection |
| title_full_unstemmed | A Deep Learning-Based Framework for Phishing Website Detection |
| title_short | A Deep Learning-Based Framework for Phishing Website Detection |
| title_sort | deep learning based framework for phishing website detection |
| topic | Phishing detection machine learning deep learning RNN-GRU web browser extension |
| url | https://ieeexplore.ieee.org/document/9661323/ |
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