Browsing by Author "Rashidi, Florence U."

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    A framework for security improvement on usage of mobile money application based on iris biometric authentication method
    (Informa UK Limited, 2024) Rashidi, Florence U.; Mohsini, Mustafa H.; Mega, Bakari
    Offering transactions through mobile devices has many advantages, such as cashless payments, lower transaction costs, and provide employment opportunities. However, it introduces access security challenges that must be dealt with, which may allow unauthorized access, resulting in theft. This work proposes a framework to improve security on the usage of Mobile Money Services (MMS) by using two-factor authentication (2FA) of PIN and iris biometric authentication method (IRBAM). The rapid application development (RAD) approach was used to develop mobile money applications based on the proposed framework. The proposed framework will improve the security of accessing MMS.
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    Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions
    (Emerald Publishing Limited, 2023) Chanhemo, Wilson Charles; Mohsini, Mustafa H.; Mjahidi, Mohamedi M.; Rashidi, Florence U.
    Purpose – This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks. Design/methodology/approach – The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research. Findings – Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks. Originality/value – This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.
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    Deep learning for SDN-enabled campus networks: proposed solutions, challenges and future directions
    (Emerald Publishing Limited, 2023) Chanhemo, Wilson Charles; Mohsini, Mustafa H.; Mjahidi, Mohamedi M.; Rashidi, Florence U.
    Purpose – This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks. Design/methodology/approach – The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research. Findings – Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks. Originality/value – This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.