Singano, Zothile TNgejane, Hombakazi CMudau, Tshimangadzo CNdlovu, LungisaniTyukala, Mkhululi2024-02-052024-02-052023-11Singano, Z.T., Ngejane, H.C., Mudau, T.C., Ndlovu, L. & Tyukala, M. 2023. ML-based security analytics in South African SMEs: A review and classification. http://hdl.handle.net/10204/13557 .979-8-3503-2781-6DOI: 10.1109/ICECET58911.2023.10389479http://hdl.handle.net/10204/13557In recent decades, the number of internet users has grown rapidly, leading to an increase in the number of cybercriminal activities. As a result, the research community has presented many cybersecurity studies to predict and prevent these activities from occurring. Cybersecurity is a crucial defence mechanism that safeguards digital assets, data, and online interactions, playing an indispensable role in maintaining the integrity, confidentiality, and availability of information in today's interconnected world. However, based on our comprehensive research, a noticeable gap was highlighted, indicating limited studies that specifically address Small and Medium Enterprises (SMEs), with a pronounced scarcity in the South African context. Predominantly, existing research has focused on the implementation of cybersecurity analytics for larger corporations. Therefore, this article is an exploration of cybersecurity analytics for small businesses in South Africa. It aims to enrich the current understanding of security analytics in SMEs by highlighting use cases, security issues involved, and what areas of research still need further exploration. These issues are then categorised and discussed to put into context how machine learning-driven security analytics can be used in SMEs to take proactive measures against cyber threats so that they protect their systems, networks, and digital assets.AbstractenCybersecurityMachine learningSecurity analyticsSmall and Medium EnterprisesSMEsConference PresentationSingano, Z. T., Ngejane, H. C., Mudau, T. C., Ndlovu, L., & Tyukala, M. (2023). ML-based security analytics in South African SMEs: A review and classification. http://hdl.handle.net/10204/13557Singano, Zothile T, Hombakazi C Ngejane, Tshimangadzo C Mudau, Lungisani Ndlovu, and Mkhululi Tyukala. "ML-based security analytics in South African SMEs: A review and classification." <i>3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 16-17 November 2023</i> (2023): http://hdl.handle.net/10204/13557Singano ZT, Ngejane HC, Mudau TC, Ndlovu L, Tyukala M, ML-based security analytics in South African SMEs: A review and classification; 2023. http://hdl.handle.net/10204/13557 .TY - Conference Presentation AU - Singano, Zothile T AU - Ngejane, Hombakazi C AU - Mudau, Tshimangadzo C AU - Ndlovu, Lungisani AU - Tyukala, Mkhululi AB - In recent decades, the number of internet users has grown rapidly, leading to an increase in the number of cybercriminal activities. As a result, the research community has presented many cybersecurity studies to predict and prevent these activities from occurring. Cybersecurity is a crucial defence mechanism that safeguards digital assets, data, and online interactions, playing an indispensable role in maintaining the integrity, confidentiality, and availability of information in today's interconnected world. However, based on our comprehensive research, a noticeable gap was highlighted, indicating limited studies that specifically address Small and Medium Enterprises (SMEs), with a pronounced scarcity in the South African context. Predominantly, existing research has focused on the implementation of cybersecurity analytics for larger corporations. Therefore, this article is an exploration of cybersecurity analytics for small businesses in South Africa. It aims to enrich the current understanding of security analytics in SMEs by highlighting use cases, security issues involved, and what areas of research still need further exploration. These issues are then categorised and discussed to put into context how machine learning-driven security analytics can be used in SMEs to take proactive measures against cyber threats so that they protect their systems, networks, and digital assets. DA - 2023-11 DB - ResearchSpace DP - CSIR J1 - 3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 16-17 November 2023 KW - Cybersecurity KW - Machine learning KW - Security analytics KW - Small and Medium Enterprises KW - SMEs LK - https://researchspace.csir.co.za PY - 2023 SM - 979-8-3503-2781-6 T1 - ML-based security analytics in South African SMEs: A review and classification TI - ML-based security analytics in South African SMEs: A review and classification UR - http://hdl.handle.net/10204/13557 ER -27415