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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.11619 |
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| _version_ | 1866914454354001920 |
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| author | Al-Subaiey, Abdulla Al-Thani, Mohammed Alam, Naser Abdullah Antora, Kaniz Fatema Khandakar, Amith Zaman, SM Ashfaq Uz |
| author_facet | Al-Subaiey, Abdulla Al-Thani, Mohammed Alam, Naser Abdullah Antora, Kaniz Fatema Khandakar, Amith Zaman, SM Ashfaq Uz |
| contents | Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in existing research, such as reliance on proprietary datasets and lack of real-world application, by proposing a high-performance machine learning model for email classification. Utilizing a comprehensive and largest available public dataset, the model achieves a f1 score of 0.99 and is designed for deployment within relevant applications. Additionally, Explainable AI (XAI) is integrated to enhance user trust. This research offers a practical and highly accurate solution, contributing to the fight against phishing by empowering users with a real-time web-based application for phishing email detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection Al-Subaiey, Abdulla Al-Thani, Mohammed Alam, Naser Abdullah Antora, Kaniz Fatema Khandakar, Amith Zaman, SM Ashfaq Uz Machine Learning Artificial Intelligence Phishing emails continue to pose a significant threat, causing financial losses and security breaches. This study addresses limitations in existing research, such as reliance on proprietary datasets and lack of real-world application, by proposing a high-performance machine learning model for email classification. Utilizing a comprehensive and largest available public dataset, the model achieves a f1 score of 0.99 and is designed for deployment within relevant applications. Additionally, Explainable AI (XAI) is integrated to enhance user trust. This research offers a practical and highly accurate solution, contributing to the fight against phishing by empowering users with a real-time web-based application for phishing email detection. |
| title | Novel Interpretable and Robust Web-based AI Platform for Phishing Email Detection |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.11619 |