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A review of machine learning and deep learning techniques for anomaly detection in iot data

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dc.citedby25
dc.contributor.authorAl-Amri R.en_US
dc.contributor.authorMurugesan R.K.en_US
dc.contributor.authorMan M.en_US
dc.contributor.authorAbdulateef A.F.en_US
dc.contributor.authorAl-Sharafi M.A.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorid57224896623en_US
dc.contributor.authorid57198406478en_US
dc.contributor.authorid24833368300en_US
dc.contributor.authorid57202801835en_US
dc.contributor.authorid57196477711en_US
dc.contributor.authorid55646765500en_US
dc.date.accessioned2023-05-29T09:07:16Z
dc.date.available2023-05-29T09:07:16Z
dc.date.issued2021
dc.description.abstractAnomaly detection has gained considerable attention in the past couple of years. Emerging technologies, such as the Internet of Things (IoT), are known to be among the most critical sources of data streams that produce massive amounts of data continuously from numerous applications. Examining these collected data to detect suspicious events can reduce functional threats and avoid unseen issues that cause downtime in the applications. Due to the dynamic nature of the data stream characteristics, many unresolved problems persist. In the existing literature, methods have been designed and developed to evaluate certain anomalous behaviors in IoT data stream sources. However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing. Thus, this paper attempts to fill this gap by providing a complete image of various state-of-the-art techniques on the major problems and core challenges in IoT data. The nature of data, anomaly types, learning mode, window model, datasets, and evaluation criteria are also presented. Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity of data, accuracy, and large-scale and high-dimensional data are investigated. Finally, the challenges that require substantial research efforts and future directions are summarized. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5320
dc.identifier.doi10.3390/app11125320
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85108551222
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85108551222&doi=10.3390%2fapp11125320&partnerID=40&md5=cfed7a38c5d2c1778975a1de0ab39909
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26155
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleA review of machine learning and deep learning techniques for anomaly detection in iot dataen_US
dc.typeReviewen_US
dspace.entity.typePublication
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