The use of multi-task learning in cybersecurity applications: a systematic literature review
<p dir="ltr">Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has be...
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2024
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| _version_ | 1864513540539285504 |
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| author | Shimaa Ibrahim (22155739) |
| author2 | Cagatay Catal (6897842) Thabet Kacem (22155742) |
| author2_role | author author |
| author_facet | Shimaa Ibrahim (22155739) Cagatay Catal (6897842) Thabet Kacem (22155742) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shimaa Ibrahim (22155739) Cagatay Catal (6897842) Thabet Kacem (22155742) |
| dc.date.none.fl_str_mv | 2024-09-27T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00521-024-10436-3 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_use_of_multi-task_learning_in_cybersecurity_applications_a_systematic_literature_review/30024148 |
| 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 Machine learning Cybersecurity Multi-task learning Cyber threats Deep learning |
| dc.title.none.fl_str_mv | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10436-3" target="_blank">https://dx.doi.org/10.1007/s00521-024-10436-3</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_941f18cf88b188c6e88ed631af978340 |
| identifier_str_mv | 10.1007/s00521-024-10436-3 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30024148 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The use of multi-task learning in cybersecurity applications: a systematic literature reviewShimaa Ibrahim (22155739)Cagatay Catal (6897842)Thabet Kacem (22155742)Information and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceMachine learningCybersecurityMulti-task learningCyber threatsDeep learning<p dir="ltr">Cybersecurity is crucial in today’s interconnected world, as digital technologies are increasingly used in various sectors. The risk of cyberattacks targeting financial, military, and political systems has increased due to the wide use of technology. Cybersecurity has become vital in information technology, with data protection being a major priority. Despite government and corporate efforts, cybersecurity remains a significant concern. The application of multi-task learning (MTL) in cybersecurity is a promising solution, allowing security systems to simultaneously address various tasks and adapt in real-time to emerging threats. While researchers have applied MTL techniques for different purposes, a systematic overview of the state-of-the-art on the role of MTL in cybersecurity is lacking. Therefore, we carried out a systematic literature review (SLR) on the use of MTL in cybersecurity applications and explored its potential applications and effectiveness in developing security measures. Five critical applications, such as network intrusion detection and malware detection, were identified, and several tasks used in these applications were observed. Most of the studies used supervised learning algorithms, and there were very limited studies that focused on other types of machine learning. This paper outlines various models utilized in the context of multi-task learning within cybersecurity and presents several challenges in this field.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-024-10436-3" target="_blank">https://dx.doi.org/10.1007/s00521-024-10436-3</a></p>2024-09-27T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00521-024-10436-3https://figshare.com/articles/journal_contribution/The_use_of_multi-task_learning_in_cybersecurity_applications_a_systematic_literature_review/30024148CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/300241482024-09-27T09:00:00Z |
| spellingShingle | The use of multi-task learning in cybersecurity applications: a systematic literature review Shimaa Ibrahim (22155739) Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Machine learning Cybersecurity Multi-task learning Cyber threats Deep learning |
| status_str | publishedVersion |
| title | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| title_full | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| title_fullStr | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| title_full_unstemmed | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| title_short | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| title_sort | The use of multi-task learning in cybersecurity applications: a systematic literature review |
| topic | Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Machine learning Cybersecurity Multi-task learning Cyber threats Deep learning |