# A Node For African Thought

Research and Innovation

To achieve and maintain the University’s vision and mission, the research and innovation division contributes mainly by promoting the research related goal of the institution which is: to drive growth in UNIZULU research and Innovation

Mr Skhumbuzo Goodwill Zwane

Faculty: Science, Agriculture and Engineering

Department: Computer Science

Discipline: Computer Science

Contact details

Email: ZwaneS@unizulu.ac.za 

Campus: KwaDlangezwa

Impactful Research Publications

(Author(s); Year; Title; journal / publisher)

T. J. Sefara, S. G. Zwane, N. Gama, H. Sibisi, P. N. Senoamadi and V. Marivate, "Transformer-based Machine Translation for Low-resourced Languages embedded with Language Identification," 2021 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa, 2021, pp. 127-132, doi: 10.1109/ICTAS50802.2021.9394996. – The study explores the use of Machine Neural Translation for Low-resourced languages in South Africa

S. Zwane, P. Tarwireyi and M. Adigun, "A Flow-based IDS for SDN-enabled Tactical Networks," 2019 International Multidisciplinary Information Technology and Engineering Conference (IMITEC), Vanderbijlpark, South Africa, 2019, pp. 1-6, doi: 10.1109/IMITEC45504.2019.9015900. – An intrusion detection system for tactical networks based on software defined networks in proposed in this study.

S. Zwane, P. Tarwireyi and M. Adigun, "Ensemble Learning Approach for Flow-based Intrusion Detection System," 2019 IEEE AFRICON, Accra, Ghana, 2019, pp. 1-8, doi: 10.1109/AFRICON46755.2019.9133979. – This study investigates the use of ensemble learning techniques for network intrusion detection with the aim of improving detection accuracy.

S. Zwane, P. Tarwireyi and M. Adigun, "Performance Analysis of Machine Learning Classifiers for Intrusion Detection," 2018 International Conference on Intelligent and Innovative Computing Applications (ICONIC), Mon Tresor, Mauritius, 2018, pp. 1-5, doi: 10.1109/ICONIC.2018.8601203. - This study analyses different machine learning techniques and assesses their performance for intrusion detection tasks