Sebopelo, RIsong, BGasela, NAbu-Mahfouz, Adnan MI2022-03-142022-03-142021-11Sebopelo, R., Isong, B., Gasela, N. & Abu-Mahfouz, A.M. 2021. A review of intrusion detection techniques in the SDN environment. http://hdl.handle.net/10204/12324 .978-1-6654-1749-5978-1-6654-1750-1DOI: 10.1109/IMITEC52926.2021.9714581http://hdl.handle.net/10204/12324Despite the advantages of Software-defined networking (SDN) over the traditional networks, SDN is facing several challenges such as security threats and attacks, dominated by a distributed denial of service (DDoS) attacks that target the controller. In recent years, the SDN has witnessed several research attentions leading to proposals and the development of countermeasures such as intrusion detection systems (IDS). IDS plays a critical role in detecting and preventing malicious activities on the networks. Several detection techniques have been exploited for the effectiveness of the IDS such as pattern matching, anomaly-based and specification-based. With the nature of SDN architecture, flow-based anomaly detection has been effective and commendable. Therefore, this paper conducted a review of some of the IDS schemes in the SDN environment. It was aimed to identify the solution offers, techniques, challenges and provide research directions. The findings show that IDS in the SDN is an active research area and several techniques exist and are dominated by machine learning (ML) which exploits the network traffic flow to detect abnormal behaviours. Intrusion detection on the SDN is still at large and more ML techniques needs to be explored, considering the critically of the SDN controller.AbstractenAnomaly FlowbasedDDoS attackIntrusion detection systemsIDSMachine learningSoftware-Defined NetworkingSDNConference PresentationSebopelo, R., Isong, B., Gasela, N., & Abu-Mahfouz, A. M. (2021). A review of intrusion detection techniques in the SDN environment. http://hdl.handle.net/10204/12324Sebopelo, R, B Isong, N Gasela, and Adnan MI Abu-Mahfouz. "A review of intrusion detection techniques in the SDN environment." <i>The 3rd International Multidisciplinary Information Technology and Engineering Conference 2021, Windhoek, Namibia, 23 - 25 November 2021</i> (2021): http://hdl.handle.net/10204/12324Sebopelo R, Isong B, Gasela N, Abu-Mahfouz AM, A review of intrusion detection techniques in the SDN environment; 2021. http://hdl.handle.net/10204/12324 .TY - Conference Presentation AU - Sebopelo, R AU - Isong, B AU - Gasela, N AU - Abu-Mahfouz, Adnan MI AB - Despite the advantages of Software-defined networking (SDN) over the traditional networks, SDN is facing several challenges such as security threats and attacks, dominated by a distributed denial of service (DDoS) attacks that target the controller. In recent years, the SDN has witnessed several research attentions leading to proposals and the development of countermeasures such as intrusion detection systems (IDS). IDS plays a critical role in detecting and preventing malicious activities on the networks. Several detection techniques have been exploited for the effectiveness of the IDS such as pattern matching, anomaly-based and specification-based. With the nature of SDN architecture, flow-based anomaly detection has been effective and commendable. Therefore, this paper conducted a review of some of the IDS schemes in the SDN environment. It was aimed to identify the solution offers, techniques, challenges and provide research directions. The findings show that IDS in the SDN is an active research area and several techniques exist and are dominated by machine learning (ML) which exploits the network traffic flow to detect abnormal behaviours. Intrusion detection on the SDN is still at large and more ML techniques needs to be explored, considering the critically of the SDN controller. DA - 2021-11 DB - ResearchSpace DP - CSIR J1 - The 3rd International Multidisciplinary Information Technology and Engineering Conference 2021, Windhoek, Namibia, 23 - 25 November 2021 KW - Anomaly Flowbased KW - DDoS attack KW - Intrusion detection systems KW - IDS KW - Machine learning KW - Software-Defined Networking KW - SDN LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-1749-5 SM - 978-1-6654-1750-1 T1 - A review of intrusion detection techniques in the SDN environment TI - A review of intrusion detection techniques in the SDN environment UR - http://hdl.handle.net/10204/12324 ER -25462