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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorDjenouri, Djamel
dc.contributor.authorBelhadi, Asma
dc.contributor.authorSrivastava, Gautam
dc.contributor.authorLin, Jerry Chun-Wei
dc.date.accessioned2022-04-27T10:35:36Z
dc.date.available2022-04-27T10:35:36Z
dc.date.created2021-12-24T23:45:51Z
dc.date.issued2021
dc.identifier.citationDjenouri, Y., Djenouri, D., Belhadi, A., Srivastava, G., & Lin, J. C.-W. (2021). Emergent deep learning for anomaly detection in internet of everything. IEEE Internet of Things Journal.en_US
dc.identifier.issn2327-4662
dc.identifier.urihttps://hdl.handle.net/11250/2992998
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.description.abstractThis research presents a new generic deep learning framework for anomaly detection in the Internet of Everything (IoE). It combines decomposition methods, deep neural networks, and evolutionary computation to better detect outliers in IoE environments. The dataset is first decomposed into clusters, while similar observations in the same cluster are grouped. Five clustering algorithms were used for this purpose. The generated clusters are then trained using Deep Learning architectures. In this context, we propose a new recurrent neural network for training time series data. Two evolutionary computational algorithms are also proposed: the genetic and the bee swarm to fine-tune the training step. These algorithms consider the hyper-parameters of the trained models and try to find the optimal values. The proposed solutions have been experimentally evaluated for two use cases: 1) road traffic outlier detection and 2) network intrusion detection. The results show the advantages of the proposed solutions and a clear superiority compared to state-of-the-art approaches.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.titleEmergent Deep Learning for Anomaly Detection in Internet of Everythingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.rights.holder© 2021 IEEEen_US
dc.source.pagenumber9en_US
dc.source.journalIEEE Internet of Things Journalen_US
dc.identifier.doi10.1109/JIOT.2021.3134932
dc.identifier.cristin1971977
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


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