Novel Hybrid UNet++ and LSTM Model for Enhanced Attack Detection and Classification in IoMT Traffic

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dc.contributor.authorMezina, Anzhelikacs
dc.contributor.authorNurmi, Jarics
dc.contributor.authorOmetov, Aleksandrcs
dc.coverage.issueAprilcs
dc.coverage.volume13cs
dc.date.accessioned2025-05-30T12:56:00Z
dc.date.available2025-05-30T12:56:00Z
dc.date.issued2025-03-24cs
dc.description.abstractThe Internet of Medical Things (IoMT) transforms healthcare by allowing real-time monitoring, diagnosis, and treatment using interconnected medical devices and sensors. However, the rapid growth of IoMT brings significant security and privacy challenges due to its critical vulnerability to cyber-attacks. This paper introduces a novel deep learning approach designed to analyze IoMT traffic and identify malicious activities. By leveraging the CIC IoMT dataset, we improved an existing neural network model to improve prediction accuracy. Our approach combines UNet++ and Long Short-Term Memory (LSTM) models to extract network traffic features effectively. Experimental results show that the proposed model outperforms traditional algorithms, achieving an accuracy of 99.92% in anomaly detection and 87.96% in attack categorization. Finally, we highlight the main limitations as well as possibilities for real-world implementation of the approach.en
dc.formattextcs
dc.format.extent57589-57603cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationIEEE Access. 2025, vol. 13, issue April, p. 57589-57603.en
dc.identifier.doi10.1109/ACCESS.2025.3553966cs
dc.identifier.issn2169-3536cs
dc.identifier.other197513cs
dc.identifier.urihttps://hdl.handle.net/11012/251191
dc.language.isoencs
dc.publisherIEEEcs
dc.relation.ispartofIEEE Accesscs
dc.relation.urihttps://ieeexplore.ieee.org/document/10937494cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2169-3536/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectAnomaly detectionen
dc.subjectClassificationen
dc.subjectIoMTen
dc.subjectHealthcareen
dc.subjectLSTMen
dc.subjectNetwork trafficen
dc.subjectUNet++en
dc.titleNovel Hybrid UNet++ and LSTM Model for Enhanced Attack Detection and Classification in IoMT Trafficen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-197513en
sync.item.dbtypeVAVen
sync.item.insts2025.05.30 14:56:00en
sync.item.modts2025.05.30 14:32:55en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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