Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection
<p dir="ltr">Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly signifi...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513550791213056 |
|---|---|
| author | Shakeel Ahmad (702854) |
| author2 | Muhammad Zaman (66868) Ahmad Sami AL-Shamayleh (20748839) Tanzila Kehkashan (20748842) Rahiel Ahmad (20748845) Safi’ I Muhammad Abdulhamid (20748848) Ismail Ergen (20748851) Adnan Akhunzada (20151648) |
| author2_role | author author author author author author author |
| author_facet | Shakeel Ahmad (702854) Muhammad Zaman (66868) Ahmad Sami AL-Shamayleh (20748839) Tanzila Kehkashan (20748842) Rahiel Ahmad (20748845) Safi’ I Muhammad Abdulhamid (20748848) Ismail Ergen (20748851) Adnan Akhunzada (20151648) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shakeel Ahmad (702854) Muhammad Zaman (66868) Ahmad Sami AL-Shamayleh (20748839) Tanzila Kehkashan (20748842) Rahiel Ahmad (20748845) Safi’ I Muhammad Abdulhamid (20748848) Ismail Ergen (20748851) Adnan Akhunzada (20151648) |
| dc.date.none.fl_str_mv | 2024-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcoms.2024.3462503 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Across_the_Spectrum_In-Depth_Review_AI-Based_Models_for_Phishing_Detection/28442150 |
| 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 Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Anomaly Detection Blocklists Cyber-Attack Mitigation Cybersecurity Deep Learning (DL) Machine Learning (ML) Phishing Detection Threat Intelligence Web Phishing Detection Whitelists |
| dc.title.none.fl_str_mv | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly significant among web phishing techniques. In a phishing attack, the attacker creates a fake website that closely resembles a legitimate one to deceive users into providing sensitive information. These attacks can be detected using both traditional and modern AI-based models. However, even with state-of-the-art methods, accurately classifying newly emerged links as phishing or legitimate remains a challenge. This study conducts a comparative analysis of more than 130 articles published between 2020 and 2024, identifying challenges and gaps in the literature and comparing the findings of various authors. The novelty of this research lies in providing a roadmap for researchers, practitioners, and cybersecurity experts to navigate the landscape of machine learning (ML) and deep learning (DL) models for phishing detection. The study reviews traditional phishing detection methods, ML and DL models, phishing datasets, and the step-by-step phishing process. It highlights limitations, research gaps, weaknesses, and potential improvements. Accuracy measures are used to compare model performance. In conclusion, this research provides a comprehensive survey of website phishing detection using AI models, offering a new roadmap for future studies</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<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/10.1109/ojcoms.2024.3462503" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3462503</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_34459fc81b41489f34e8fbcdaaed85cc |
| identifier_str_mv | 10.1109/ojcoms.2024.3462503 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/28442150 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Across the Spectrum In-Depth Review AI-Based Models for Phishing DetectionShakeel Ahmad (702854)Muhammad Zaman (66868)Ahmad Sami AL-Shamayleh (20748839)Tanzila Kehkashan (20748842)Rahiel Ahmad (20748845)Safi’ I Muhammad Abdulhamid (20748848)Ismail Ergen (20748851)Adnan Akhunzada (20151648)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningAnomaly DetectionBlocklistsCyber-Attack MitigationCybersecurityDeep Learning (DL)Machine Learning (ML)Phishing DetectionThreat IntelligenceWeb Phishing DetectionWhitelists<p dir="ltr">Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly significant among web phishing techniques. In a phishing attack, the attacker creates a fake website that closely resembles a legitimate one to deceive users into providing sensitive information. These attacks can be detected using both traditional and modern AI-based models. However, even with state-of-the-art methods, accurately classifying newly emerged links as phishing or legitimate remains a challenge. This study conducts a comparative analysis of more than 130 articles published between 2020 and 2024, identifying challenges and gaps in the literature and comparing the findings of various authors. The novelty of this research lies in providing a roadmap for researchers, practitioners, and cybersecurity experts to navigate the landscape of machine learning (ML) and deep learning (DL) models for phishing detection. The study reviews traditional phishing detection methods, ML and DL models, phishing datasets, and the step-by-step phishing process. It highlights limitations, research gaps, weaknesses, and potential improvements. Accuracy measures are used to compare model performance. In conclusion, this research provides a comprehensive survey of website phishing detection using AI models, offering a new roadmap for future studies</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<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/10.1109/ojcoms.2024.3462503" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3462503</a></p>2024-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcoms.2024.3462503https://figshare.com/articles/journal_contribution/Across_the_Spectrum_In-Depth_Review_AI-Based_Models_for_Phishing_Detection/28442150CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/284421502024-01-01T00:00:00Z |
| spellingShingle | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection Shakeel Ahmad (702854) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Anomaly Detection Blocklists Cyber-Attack Mitigation Cybersecurity Deep Learning (DL) Machine Learning (ML) Phishing Detection Threat Intelligence Web Phishing Detection Whitelists |
| status_str | publishedVersion |
| title | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| title_full | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| title_fullStr | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| title_full_unstemmed | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| title_short | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| title_sort | Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Anomaly Detection Blocklists Cyber-Attack Mitigation Cybersecurity Deep Learning (DL) Machine Learning (ML) Phishing Detection Threat Intelligence Web Phishing Detection Whitelists |