Masengo Wa Umba, SRamotsoela, TDAbu-Mahfouz, Adnan MIHancke, GP2019-10-142019-10-142019-06Masengo Wa Umba, S., Ramotsoela, T.D., Abu-Mahfouz, A.M.I. and Hancke, G.P. 2019. Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks. IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, Canada, 12-14 June 2019 pp 2220-2225978-1-7281-3666-0978-1-7281-3667-7https://ieeexplore.ieee.org/document/8781114DOI: 10.1109/ISIE.2019.8781114http://hdl.handle.net/10204/11167Copyright: 2019 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, kindly consult the publisher's website.Nowadays, Wireless Sensor Networks (WSNs) are intensively used in highly sensitive environments such as water treatment plants, airports and hospitals. For this reason, the security of communications in WSNs is a very critical problem that must be tackled accordingly. A Software-defined network (SDN) is an architecture aimed at making networks more agile and flexible. A Software-Defined Wireless Sensor Network (SDWSN) is realized by infusing a Software Defined Network (SDN) model in a WSN. In this paper, three Artificial Intelligence (AI) approaches (decision tree, naïve Bayes and deep artificial neural network) used as intrusion detection systems (IDSs) in SDWSNs are analyzed and the results show that the decision tree approach is the best approach for implementing IDSs in classical SDWSNs given its performances.enArtificial IntelligenceAISoftware-Defined Wireless Sensor NetworkSDWSNWireless Sensor NetworksWSNConference PresentationMasengo Wa Umba, S., Ramotsoela, T., Abu-Mahfouz, A. M., & Hancke, G. (2019). Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks. IEEE. http://hdl.handle.net/10204/11167Masengo Wa Umba, S, TD Ramotsoela, Adnan MI Abu-Mahfouz, and GP Hancke. "Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks." (2019): http://hdl.handle.net/10204/11167Masengo Wa Umba S, Ramotsoela T, Abu-Mahfouz AM, Hancke G, Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks; IEEE; 2019. http://hdl.handle.net/10204/11167 .TY - Conference Presentation AU - Masengo Wa Umba, S AU - Ramotsoela, TD AU - Abu-Mahfouz, Adnan MI AU - Hancke, GP AB - Nowadays, Wireless Sensor Networks (WSNs) are intensively used in highly sensitive environments such as water treatment plants, airports and hospitals. For this reason, the security of communications in WSNs is a very critical problem that must be tackled accordingly. A Software-defined network (SDN) is an architecture aimed at making networks more agile and flexible. A Software-Defined Wireless Sensor Network (SDWSN) is realized by infusing a Software Defined Network (SDN) model in a WSN. In this paper, three Artificial Intelligence (AI) approaches (decision tree, naïve Bayes and deep artificial neural network) used as intrusion detection systems (IDSs) in SDWSNs are analyzed and the results show that the decision tree approach is the best approach for implementing IDSs in classical SDWSNs given its performances. DA - 2019-06 DB - ResearchSpace DP - CSIR KW - Artificial Intelligence KW - AI KW - Software-Defined Wireless Sensor Network KW - SDWSN KW - Wireless Sensor Networks KW - WSN LK - https://researchspace.csir.co.za PY - 2019 SM - 978-1-7281-3666-0 SM - 978-1-7281-3667-7 T1 - Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks TI - Comparative study of artificial intelligence based intrusion detection for Software-Defined Wireless Sensor Networks UR - http://hdl.handle.net/10204/11167 ER -