Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems
<p>Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing tec...
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2021
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| _version_ | 1864513560469569536 |
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| author | Aliya Tabassum (16896486) |
| author2 | Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| author2_role | author author author |
| author_facet | Aliya Tabassum (16896486) Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aliya Tabassum (16896486) Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| dc.date.none.fl_str_mv | 2021-01-13T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3051530 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Privacy-Preserving_Distributed_IDS_Using_Incremental_Learning_for_IoT_Health_Systems/24049365 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Feature extraction Deep learning Security Real-time systems Intrusion detection Data models Task analysis Internet of things (IoT) Intrusion detection system (IDS) Incremental learning Pre-processing |
| dc.title.none.fl_str_mv | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" 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.2021.3051530" target="_blank">https://dx.doi.org/10.1109/access.2021.3051530</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7bf03fa2c50b91aee44600539eefdf8c |
| identifier_str_mv | 10.1109/access.2021.3051530 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049365 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health SystemsAliya Tabassum (16896486)Aiman Erbad (14150589)Amr Mohamed (3508121)Mohsen Guizani (12580291)EngineeringBiomedical engineeringInformation and computing sciencesCybersecurity and privacyDistributed computing and systems softwareMachine learningFeature extractionDeep learningSecurityReal-time systemsIntrusion detectionData modelsTask analysisInternet of things (IoT)Intrusion detection system (IDS)Incremental learningPre-processing<p>Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection pipeline for distributed incremental learning. Our pre-processing technique eliminates redundancies and selects unique features by following innovative extraction techniques. We use autoencoders with non-negativity constraints, which help us extract less redundant features. More importantly, the distributed intrusion detection model reduces the burden on the edge classifier and distributes the load among IoT and edge devices. Theoretical analysis and numerical experiments have shown lower space and time costs than state of the art techniques, with comparable classification accuracy. Extensive experiments with standard data sets and real-time streaming IoT traffic give encouraging results.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" 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.2021.3051530" target="_blank">https://dx.doi.org/10.1109/access.2021.3051530</a></p>2021-01-13T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3051530https://figshare.com/articles/journal_contribution/Privacy-Preserving_Distributed_IDS_Using_Incremental_Learning_for_IoT_Health_Systems/24049365CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240493652021-01-13T00:00:00Z |
| spellingShingle | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems Aliya Tabassum (16896486) Engineering Biomedical engineering Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Feature extraction Deep learning Security Real-time systems Intrusion detection Data models Task analysis Internet of things (IoT) Intrusion detection system (IDS) Incremental learning Pre-processing |
| status_str | publishedVersion |
| title | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| title_full | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| title_fullStr | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| title_full_unstemmed | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| title_short | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| title_sort | Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems |
| topic | Engineering Biomedical engineering Information and computing sciences Cybersecurity and privacy Distributed computing and systems software Machine learning Feature extraction Deep learning Security Real-time systems Intrusion detection Data models Task analysis Internet of things (IoT) Intrusion detection system (IDS) Incremental learning Pre-processing |