Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems
<p dir="ltr">Nowadays, emails are used across almost every field, spanning from business to education. Broadly, emails can be categorized as either ham or spam. Email spam, also known as junk emails or unwanted emails, can harm users by wasting time and computing resources, along wit...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513543135559680 |
|---|---|
| author | Ekramul Haque Tusher (21324362) |
| author2 | Mohd Arfian Ismail (18522493) Md Arafatur Rahman (9316568) Ali H. Alenezi (21324365) Mueen Uddin (4903510) |
| author2_role | author author author author |
| author_facet | Ekramul Haque Tusher (21324362) Mohd Arfian Ismail (18522493) Md Arafatur Rahman (9316568) Ali H. Alenezi (21324365) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ekramul Haque Tusher (21324362) Mohd Arfian Ismail (18522493) Md Arafatur Rahman (9316568) Ali H. Alenezi (21324365) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2024-09-25T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2024.3467996 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Email_Spam_A_Comprehensive_Review_of_Optimize_Detection_Methods_Challenges_and_Open_Research_Problems/29605226 |
| 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 Human-centred computing Machine learning Software engineering Email spam Machine learning Deep learning Fuzzy system Feature selection Spam detection |
| dc.title.none.fl_str_mv | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Nowadays, emails are used across almost every field, spanning from business to education. Broadly, emails can be categorized as either ham or spam. Email spam, also known as junk emails or unwanted emails, can harm users by wasting time and computing resources, along with stealing valuable information. The volume of spam emails is rising rapidly day by day. Detecting and filtering spam presents significant and complex challenges for email systems. Traditional identification techniques like blocklists, real-time blackhole listing, and content-based methods have limitations. These limitations have led to the advancement of more sophisticated machine learning (ML) and deep learning (DL) methods for enhanced spam detection accuracy. In recent years, considerable attention has focused on the potential of ML and DL methods to improve email spam detection. A comprehensive literature review is therefore imperative for developing an updated, evidence-based understanding of contemporary research on employing these methods against this persistent problem. The review aims to systematically identify various ML and DL methods applied for spam detection, evaluate their effectiveness, and highlight promising future research directions considering gaps. By combining and analyzing findings across studies, it will obtain the strengths and weaknesses of existing methods. This review seeks to advance knowledge on reliable and efficient integration of state-of-the-art ML and DL into identifying email spam.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3467996" target="_blank">https://dx.doi.org/10.1109/access.2024.3467996</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_baf82c0afc23533f9f85e0b291b6d479 |
| identifier_str_mv | 10.1109/access.2024.3467996 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605226 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research ProblemsEkramul Haque Tusher (21324362)Mohd Arfian Ismail (18522493)Md Arafatur Rahman (9316568)Ali H. Alenezi (21324365)Mueen Uddin (4903510)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceHuman-centred computingMachine learningSoftware engineeringEmail spamMachine learningDeep learningFuzzy systemFeature selectionSpam detection<p dir="ltr">Nowadays, emails are used across almost every field, spanning from business to education. Broadly, emails can be categorized as either ham or spam. Email spam, also known as junk emails or unwanted emails, can harm users by wasting time and computing resources, along with stealing valuable information. The volume of spam emails is rising rapidly day by day. Detecting and filtering spam presents significant and complex challenges for email systems. Traditional identification techniques like blocklists, real-time blackhole listing, and content-based methods have limitations. These limitations have led to the advancement of more sophisticated machine learning (ML) and deep learning (DL) methods for enhanced spam detection accuracy. In recent years, considerable attention has focused on the potential of ML and DL methods to improve email spam detection. A comprehensive literature review is therefore imperative for developing an updated, evidence-based understanding of contemporary research on employing these methods against this persistent problem. The review aims to systematically identify various ML and DL methods applied for spam detection, evaluate their effectiveness, and highlight promising future research directions considering gaps. By combining and analyzing findings across studies, it will obtain the strengths and weaknesses of existing methods. This review seeks to advance knowledge on reliable and efficient integration of state-of-the-art ML and DL into identifying email spam.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2024.3467996" target="_blank">https://dx.doi.org/10.1109/access.2024.3467996</a></p>2024-09-25T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2024.3467996https://figshare.com/articles/journal_contribution/Email_Spam_A_Comprehensive_Review_of_Optimize_Detection_Methods_Challenges_and_Open_Research_Problems/29605226CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296052262024-09-25T03:00:00Z |
| spellingShingle | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems Ekramul Haque Tusher (21324362) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Human-centred computing Machine learning Software engineering Email spam Machine learning Deep learning Fuzzy system Feature selection Spam detection |
| status_str | publishedVersion |
| title | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| title_full | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| title_fullStr | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| title_full_unstemmed | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| title_short | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| title_sort | Email Spam: A Comprehensive Review of Optimize Detection Methods, Challenges, and Open Research Problems |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Human-centred computing Machine learning Software engineering Email spam Machine learning Deep learning Fuzzy system Feature selection Spam detection |