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AuthorJiang, Jinfang
AuthorHan, Guangjie
AuthorLiu, Li
AuthorShu, Lei
AuthorGuizani, Mohsen
Available date2022-12-05T22:34:07Z
Publication Date2020-06-01
Publication NameIEEE Wireless Communications
Identifierhttp://dx.doi.org/10.1109/MWC.001.1900410
CitationJiang, J., Han, G., Shu, L., & Guizani, M. (2020). Outlier detection approaches based on machine learning in the internet-of-things. IEEE Wireless Communications, 27(3), 53-59.‏
ISSN15361284
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086889312&origin=inward
URIhttp://hdl.handle.net/10576/36949
AbstractOutlier detection in the Internet of Things (IoT) is an essential challenge issue studied in numerous fields, including fraud monitoring, intrusion detection, secure localization, trust management, and so on. Conventional outlier detection technologies cannot be used directly in IoT due to the open nature of wireless communication as well as the resource-constrained characteristics of end nodes. Therefore, this article provides a comprehensive survey of new outlier detection approaches based on machine learning for IoT. The approaches are first carefully discussed based on their adopted machine learning algorithms. In addition, the performance of them with respect to the advantages and the drawbacks are compared in detail, which naturally leads to some open research issues that are analyzed afterward.
SponsorThe work is supported by the National Key Research and Development Program, No. 2017YFE0125300, the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1801264, the Jiangsu Provincial Six Talent Peaks Project No. XYDXX-012 and the Jiangsu Key Research and Development Program, No. BE2019648 , and Project of Fujian University of Technology, No. GY-Z19066.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectAnomaly detection
TitleOutlier detection approaches based on machine learning in the internet-of-things
TypeConference Paper
Issue Number3
Volume Number27
dc.accessType Abstract Only


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