Thupae, RIsong, BGasela, NAbu-Mahfouz, Adnan MI2019-03-222019-03-222018-10Thupae, 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. 4645-4650978-1-5090-6684-1978-1-5090-6685-8https://ieeexplore.ieee.org/document/8591178DOI: 10.1109/IECON.2018.8591178http://hdl.handle.net/10204/10825Copyright: 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.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.enSoftware defined networkSDNWireless sensor networksWSNsConference PresentationThupae, R., Isong, B., Gasela, N., & Abu-Mahfouz, A. M. (2018). Machine learning techniques for traffic identification and classification in SDWSN: A survey. IEEE. http://hdl.handle.net/10204/10825Thupae, R, B Isong, N Gasela, and Adnan MI Abu-Mahfouz. "Machine learning techniques for traffic identification and classification in SDWSN: A survey." (2018): http://hdl.handle.net/10204/10825Thupae R, Isong B, Gasela N, Abu-Mahfouz AM, Machine learning techniques for traffic identification and classification in SDWSN: A survey; IEEE; 2018. http://hdl.handle.net/10204/10825 .TY - Conference Presentation AU - Thupae, R AU - Isong, B AU - Gasela, N AU - Abu-Mahfouz, Adnan MI AB - 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. DA - 2018-10 DB - ResearchSpace DP - CSIR KW - Software defined network KW - SDN KW - Wireless sensor networks KW - WSNs LK - https://researchspace.csir.co.za PY - 2018 SM - 978-1-5090-6684-1 SM - 978-1-5090-6685-8 SM - https://ieeexplore.ieee.org/document/8591178 SM - DOI: 10.1109/IECON.2018.8591178 T1 - Machine learning techniques for traffic identification and classification in SDWSN: A survey TI - Machine learning techniques for traffic identification and classification in SDWSN: A survey UR - http://hdl.handle.net/10204/10825 ER -