Email spam classification based on deep learning methods: A review
Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for...
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| Format: | Article |
| Language: | English English |
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College of Education, Al-Iraqia University
2025
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| Online Access: | http://umpir.ump.edu.my/id/eprint/42581/1/Email%20spam%20classification%20based%20on%20deep%20learning%20methods.pdf http://umpir.ump.edu.my/id/eprint/42581/7/Email%20Spam%20Classification%20Based%20on%20Deep%20Learning%20Methods.pdf |
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| author | Tusher, Ekramul Haque Mohd Arfian, Ismail Anis Farihan, Mat Raffei |
| author_facet | Tusher, Ekramul Haque Mohd Arfian, Ismail Anis Farihan, Mat Raffei |
| author_sort | Tusher, Ekramul Haque |
| collection | UMP |
| description | Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes of pertinent studies, this review delineates the strengths and drawbacks of various approaches, offering valuable insights into the challenges that must be tackled to enhance the precision and efficacy of email spam classification. |
| first_indexed | 2025-02-19T02:37:43Z |
| format | Article |
| id | UMPir42581 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2025-02-19T02:37:43Z |
| publishDate | 2025 |
| publisher | College of Education, Al-Iraqia University |
| record_format | dspace |
| spelling | UMPir425812025-02-14T01:32:55Z http://umpir.ump.edu.my/id/eprint/42581/ Email spam classification based on deep learning methods: A review Tusher, Ekramul Haque Mohd Arfian, Ismail Anis Farihan, Mat Raffei QA75 Electronic computers. Computer science Email spam is a significant issue confronting both email consumers and providers. The evolution of spam filtering has progressed considerably, transitioning from basic rule-based filters to more sophisticated machine learning algorithms. Deep learning has become a potent collection of techniques for addressing intricate issues such as spam classification in recent times. A thorough literature evaluation is required to have a comprehensive overview of the current research on utilizing deep learning methods for email spam classification. This review aims to identify the various deep learning techniques used for email spam, their effectiveness, and areas for future research. By synthesizing the outcomes of pertinent studies, this review delineates the strengths and drawbacks of various approaches, offering valuable insights into the challenges that must be tackled to enhance the precision and efficacy of email spam classification. College of Education, Al-Iraqia University 2025-02 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42581/1/Email%20spam%20classification%20based%20on%20deep%20learning%20methods.pdf pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42581/7/Email%20Spam%20Classification%20Based%20on%20Deep%20Learning%20Methods.pdf Tusher, Ekramul Haque and Mohd Arfian, Ismail and Anis Farihan, Mat Raffei (2025) Email spam classification based on deep learning methods: A review. Iraqi Journal for Computer Science and Mathematics (IJCSM), 6 (1). pp. 24-36. ISSN 2788-7421. (Published) https://doi.org/10.52866/2788-7421.1236 https://doi.org/10.52866/2788-7421.1236 |
| spellingShingle | QA75 Electronic computers. Computer science Tusher, Ekramul Haque Mohd Arfian, Ismail Anis Farihan, Mat Raffei Email spam classification based on deep learning methods: A review |
| title | Email spam classification based on deep learning methods: A review |
| title_full | Email spam classification based on deep learning methods: A review |
| title_fullStr | Email spam classification based on deep learning methods: A review |
| title_full_unstemmed | Email spam classification based on deep learning methods: A review |
| title_short | Email spam classification based on deep learning methods: A review |
| title_sort | email spam classification based on deep learning methods a review |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/42581/1/Email%20spam%20classification%20based%20on%20deep%20learning%20methods.pdf http://umpir.ump.edu.my/id/eprint/42581/7/Email%20Spam%20Classification%20Based%20on%20Deep%20Learning%20Methods.pdf |
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