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Machine learning techniques for traffic identification and classification in SDWSN: A survey

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dc.contributor.author Thupae, R
dc.contributor.author Isong, B
dc.contributor.author Gasela, N
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2019-03-22T11:43:28Z
dc.date.available 2019-03-22T11:43:28Z
dc.date.issued 2018-10
dc.identifier.citation Thupae, R. et al. 2018. Machine learning techniques for traffic identification and classification in SDWSN: A survey. IECON 2018 - The 44th Annual Conference of the IEEE Industrial Electronics Society, 21-23 October 2018, Washington D.C, USA, pp. 4645-4650 en_US
dc.identifier.isbn 978-1-5090-6684-1
dc.identifier.isbn 978-1-5090-6685-8
dc.identifier.isbn https://ieeexplore.ieee.org/document/8591178
dc.identifier.isbn DOI: 10.1109/IECON.2018.8591178
dc.identifier.uri http://hdl.handle.net/10204/10825
dc.description Copyright: 2018 IEEE. Due to copyright restrictions, the attached PDF file contains the accepted version of the published item. For access to the published version, please consult the publisher's website. en_US
dc.description.abstract Software defined network (SDN) is a paradigm developed achieve great flexibility and cope with the limitations of traditional networks architecture such as the wireless sensor networks (WSNs). Introducing SDN in WSN leads to SDWSN. However, due to the challenges that are inherent in SDN and WSN, SDWSN is faced with number of challenges such network and Internet traffic classification (TC). Several solutions have been offered such as machine learning (ML) technique but there are several challenges that still exist which need attention. Therefore, this paper present a review on the approaches of TC in SDWSN using ML and their challenges. The objective is to identify existing approaches and the challenges in order to provide ways to enhance them. We performed review of the existing works on TC in the literature based on the aspect of enterprises network, SDN and WSN has been done as well as findings reported. Our findings shows that the approaches to TC using ML were based on supervised or unsupervised learning. Moreover, TC is faced with challenges which include energy efficiency, shareable test data and design. Thus, ML technique to TC in SDWSN is still at its early stage and need to improve in order to accurately classify traffics that normal or abnormal. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;21888
dc.subject Software defined network en_US
dc.subject SDN en_US
dc.subject Wireless sensor networks en_US
dc.subject WSNs en_US
dc.title Machine learning techniques for traffic identification and classification in SDWSN: A survey en_US
dc.type Conference Presentation en_US


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