Phishing Website Detection Using Deep Learning Models
This research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ensemble learning, and deep learning algorithms. Cybersecurity is essential for protecting data and networks from...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10735206/ |
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| author | Ume Zara Kashif Ayyub Hikmat Ullah Khan Ali Daud Tariq Alsahfi Saima Gulzar Ahmad |
| author_facet | Ume Zara Kashif Ayyub Hikmat Ullah Khan Ali Daud Tariq Alsahfi Saima Gulzar Ahmad |
| author_sort | Ume Zara |
| collection | DOAJ |
| description | This research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ensemble learning, and deep learning algorithms. Cybersecurity is essential for protecting data and networks from threats. Detecting phishing websites helps prevent fraud and safeguard personal information. To evaluate the efficacy of our proposed method, the top features using information gain, gain ratio, and PCA are used to predict and identify a website as phishing or non-phishing. The proposed system is trained using a dataset that covers 11,055 websites. The ensemble learning model applied achieved an impressive 99% accuracy in predicting phishing websites, surpassing previous models, and setting a new benchmark in the field. The findings highlight the effectiveness of combining deep learning architectures with ensemble learning, offering not only improved accuracy but also adaptability to emerging phishing techniques. |
| format | Article |
| id | doaj-art-c2940756f7a24966927d5ed641e3dab4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c2940756f7a24966927d5ed641e3dab42024-11-19T00:03:31ZengIEEEIEEE Access2169-35362024-01-011216707216708710.1109/ACCESS.2024.348646210735206Phishing Website Detection Using Deep Learning ModelsUme Zara0Kashif Ayyub1Hikmat Ullah Khan2Ali Daud3https://orcid.org/0000-0002-8284-6354Tariq Alsahfi4https://orcid.org/0000-0003-4299-1626Saima Gulzar Ahmad5Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanDepartment of Information Technology, University of Sargodha, Sargodha, PakistanFaculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab EmiratesDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad, PakistanThis research addresses the imperative need for advanced detection mechanisms for the identification of phishing websites. For this purpose, we explore state-of-the-art machine learning, ensemble learning, and deep learning algorithms. Cybersecurity is essential for protecting data and networks from threats. Detecting phishing websites helps prevent fraud and safeguard personal information. To evaluate the efficacy of our proposed method, the top features using information gain, gain ratio, and PCA are used to predict and identify a website as phishing or non-phishing. The proposed system is trained using a dataset that covers 11,055 websites. The ensemble learning model applied achieved an impressive 99% accuracy in predicting phishing websites, surpassing previous models, and setting a new benchmark in the field. The findings highlight the effectiveness of combining deep learning architectures with ensemble learning, offering not only improved accuracy but also adaptability to emerging phishing techniques.https://ieeexplore.ieee.org/document/10735206/Deep learningensemble learningfeature selectionGRULSTMmachine learning |
| spellingShingle | Ume Zara Kashif Ayyub Hikmat Ullah Khan Ali Daud Tariq Alsahfi Saima Gulzar Ahmad Phishing Website Detection Using Deep Learning Models IEEE Access Deep learning ensemble learning feature selection GRU LSTM machine learning |
| title | Phishing Website Detection Using Deep Learning Models |
| title_full | Phishing Website Detection Using Deep Learning Models |
| title_fullStr | Phishing Website Detection Using Deep Learning Models |
| title_full_unstemmed | Phishing Website Detection Using Deep Learning Models |
| title_short | Phishing Website Detection Using Deep Learning Models |
| title_sort | phishing website detection using deep learning models |
| topic | Deep learning ensemble learning feature selection GRU LSTM machine learning |
| url | https://ieeexplore.ieee.org/document/10735206/ |
| work_keys_str_mv | AT umezara phishingwebsitedetectionusingdeeplearningmodels AT kashifayyub phishingwebsitedetectionusingdeeplearningmodels AT hikmatullahkhan phishingwebsitedetectionusingdeeplearningmodels AT alidaud phishingwebsitedetectionusingdeeplearningmodels AT tariqalsahfi phishingwebsitedetectionusingdeeplearningmodels AT saimagulzarahmad phishingwebsitedetectionusingdeeplearningmodels |