Novel interpretable and robust web-based AI platform for phishing email detection
<p dir="ltr">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 mach...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513555982712832 |
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| author | Abdulla Al-Subaiey (19757007) |
| author2 | Mohammed Al-Thani (4000229) Naser Abdullah Alam (19757010) Kaniz Fatema Antora (19757013) Amith Khandakar (14151981) SM Ashfaq Uz Zaman (19757016) |
| author2_role | author author author author author |
| author_facet | Abdulla Al-Subaiey (19757007) Mohammed Al-Thani (4000229) Naser Abdullah Alam (19757010) Kaniz Fatema Antora (19757013) Amith Khandakar (14151981) SM Ashfaq Uz Zaman (19757016) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abdulla Al-Subaiey (19757007) Mohammed Al-Thani (4000229) Naser Abdullah Alam (19757010) Kaniz Fatema Antora (19757013) Amith Khandakar (14151981) SM Ashfaq Uz Zaman (19757016) |
| dc.date.none.fl_str_mv | 2024-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compeleceng.2024.109625 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Novel_interpretable_and_robust_web-based_AI_platform_for_phishing_email_detection/27130080 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Phishing emails Machine learning model Email classification Dataset Explainable AI User trust Web-based application |
| dc.title.none.fl_str_mv | Novel interpretable and robust web-based AI platform for phishing email detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers and Electrical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109625" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109625</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_302c83f49946f39aeca969d944ce01ec |
| identifier_str_mv | 10.1016/j.compeleceng.2024.109625 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27130080 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Novel interpretable and robust web-based AI platform for phishing email detectionAbdulla Al-Subaiey (19757007)Mohammed Al-Thani (4000229)Naser Abdullah Alam (19757010)Kaniz Fatema Antora (19757013)Amith Khandakar (14151981)SM Ashfaq Uz Zaman (19757016)Information and computing sciencesCybersecurity and privacyData management and data scienceMachine learningPhishing emailsMachine learning modelEmail classificationDatasetExplainable AIUser trustWeb-based application<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers and Electrical Engineering<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compeleceng.2024.109625" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2024.109625</a></p>2024-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compeleceng.2024.109625https://figshare.com/articles/journal_contribution/Novel_interpretable_and_robust_web-based_AI_platform_for_phishing_email_detection/27130080CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/271300802024-12-01T00:00:00Z |
| spellingShingle | Novel interpretable and robust web-based AI platform for phishing email detection Abdulla Al-Subaiey (19757007) Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Phishing emails Machine learning model Email classification Dataset Explainable AI User trust Web-based application |
| status_str | publishedVersion |
| title | Novel interpretable and robust web-based AI platform for phishing email detection |
| title_full | Novel interpretable and robust web-based AI platform for phishing email detection |
| title_fullStr | Novel interpretable and robust web-based AI platform for phishing email detection |
| title_full_unstemmed | Novel interpretable and robust web-based AI platform for phishing email detection |
| title_short | Novel interpretable and robust web-based AI platform for phishing email detection |
| title_sort | Novel interpretable and robust web-based AI platform for phishing email detection |
| topic | Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Phishing emails Machine learning model Email classification Dataset Explainable AI User trust Web-based application |