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ML-based security analytics in South African SMEs: A review and classification

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dc.contributor.author Singano, Zothile T
dc.contributor.author Ngejane, Hombakazi C
dc.contributor.author Mudau, Tshimangadzo C
dc.contributor.author Ndlovu, Lungisani
dc.contributor.author Tyukala, Mkhululi
dc.date.accessioned 2024-02-05T07:25:10Z
dc.date.available 2024-02-05T07:25:10Z
dc.date.issued 2023-11
dc.identifier.citation Singano, 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 . en_ZA
dc.identifier.isbn 979-8-3503-2781-6
dc.identifier.uri DOI: 10.1109/ICECET58911.2023.10389479
dc.identifier.uri http://hdl.handle.net/10204/13557
dc.description.abstract 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. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/document/10389479 en_US
dc.relation.uri http://www.icecet.com/2023/ en_US
dc.relation.uri http://www.icecet.com/physical.pdf en_US
dc.source 3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 16-17 November 2023 en_US
dc.subject Cybersecurity en_US
dc.subject Machine learning en_US
dc.subject Security analytics en_US
dc.subject Small and Medium Enterprises en_US
dc.subject SMEs en_US
dc.title ML-based security analytics in South African SMEs: A review and classification en_US
dc.type Conference Presentation en_US
dc.description.pages 6 en_US
dc.description.note ©2023 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, please consult the publisher's website: https://ieeexplore.ieee.org/document/10389479 en_US
dc.description.cluster Defence and Security en_US
dc.description.impactarea Inf and Cybersecurity Centre en_US
dc.identifier.apacitation Singano, 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 en_ZA
dc.identifier.chicagocitation Singano, 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/10204/13557 en_ZA
dc.identifier.vancouvercitation Singano 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 . en_ZA
dc.identifier.ris 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 - en_ZA
dc.identifier.worklist 27415 en_US


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