Edge intelligence for network intrusion prevention in IoT ecosystem
<p>The Internet of Things (IoT) platform allows physical devices to connect directly to the internet and upload data continuously. Insecure access makes IoT platforms vulnerable to different network intrusion attacks. As a result, the Intrusion Detection System (IDS) is a core component of a m...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| منشور في: |
2023
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1864513529555451904 |
|---|---|
| author | Mansura Habiba (17808302) |
| author2 | Md. Rafiqul Islam (403453) S.M. Muyeen (15746160) A.B.M. Shawkat Ali (17808305) |
| author2_role | author author author |
| author_facet | Mansura Habiba (17808302) Md. Rafiqul Islam (403453) S.M. Muyeen (15746160) A.B.M. Shawkat Ali (17808305) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansura Habiba (17808302) Md. Rafiqul Islam (403453) S.M. Muyeen (15746160) A.B.M. Shawkat Ali (17808305) |
| dc.date.none.fl_str_mv | 2023-05-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compeleceng.2023.108727 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Edge_intelligence_for_network_intrusion_prevention_in_IoT_ecosystem/25018844 |
| 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 Distributed computing and systems software Machine learning Internet of things (IoT) IoT applications Security Attacks Privacy Machine learning Deep learning |
| dc.title.none.fl_str_mv | Edge intelligence for network intrusion prevention in IoT ecosystem |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>The Internet of Things (IoT) platform allows physical devices to connect directly to the internet and upload data continuously. Insecure access makes IoT platforms vulnerable to different network intrusion attacks. As a result, the Intrusion Detection System (IDS) is a core component of a modern IoT platform. However, traditional IDS often follows rule-based detection where the rules can be changed and exposed to the attacker and becomes weak over time. An efficient IDS also needs to be dynamic and effective in real time. This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. The performance evaluation using standard datasets demonstrates that the proposed model provides an accuracy of up to 99.99%.</p><h2>Other Information</h2> <p> 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.2023.108727" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2023.108727</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_39d0d07480121a9bbd9aada12aa84293 |
| identifier_str_mv | 10.1016/j.compeleceng.2023.108727 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25018844 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Edge intelligence for network intrusion prevention in IoT ecosystemMansura Habiba (17808302)Md. Rafiqul Islam (403453)S.M. Muyeen (15746160)A.B.M. Shawkat Ali (17808305)Information and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningInternet of things (IoT)IoT applicationsSecurityAttacksPrivacyMachine learningDeep learning<p>The Internet of Things (IoT) platform allows physical devices to connect directly to the internet and upload data continuously. Insecure access makes IoT platforms vulnerable to different network intrusion attacks. As a result, the Intrusion Detection System (IDS) is a core component of a modern IoT platform. However, traditional IDS often follows rule-based detection where the rules can be changed and exposed to the attacker and becomes weak over time. An efficient IDS also needs to be dynamic and effective in real time. This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. The performance evaluation using standard datasets demonstrates that the proposed model provides an accuracy of up to 99.99%.</p><h2>Other Information</h2> <p> 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.2023.108727" target="_blank">https://dx.doi.org/10.1016/j.compeleceng.2023.108727</a></p>2023-05-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compeleceng.2023.108727https://figshare.com/articles/journal_contribution/Edge_intelligence_for_network_intrusion_prevention_in_IoT_ecosystem/25018844CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250188442023-05-01T00:00:00Z |
| spellingShingle | Edge intelligence for network intrusion prevention in IoT ecosystem Mansura Habiba (17808302) Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Internet of things (IoT) IoT applications Security Attacks Privacy Machine learning Deep learning |
| status_str | publishedVersion |
| title | Edge intelligence for network intrusion prevention in IoT ecosystem |
| title_full | Edge intelligence for network intrusion prevention in IoT ecosystem |
| title_fullStr | Edge intelligence for network intrusion prevention in IoT ecosystem |
| title_full_unstemmed | Edge intelligence for network intrusion prevention in IoT ecosystem |
| title_short | Edge intelligence for network intrusion prevention in IoT ecosystem |
| title_sort | Edge intelligence for network intrusion prevention in IoT ecosystem |
| topic | Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Internet of things (IoT) IoT applications Security Attacks Privacy Machine learning Deep learning |