The ODL African Continental Education Strategy: Anchoring AI/Machine Learning on the African Technological Innovation and Investment Table

Abstract

Through the process of innovation, research produces knowledge and technology that are put to use in the real world. The development of applied scientific technologies can be judged by the number of technological advancements, patents, innovations, research papers that have been published, etc. The purpose of the research was to develop an economic framework and technology solutions for the development of knowledge, innovation, and enterprise on the African continent, using Zimbabwe as a representative example. The study examined the potential applications of cybersecurity and machine learning to the business of producing and disseminating information. The practice of cybersecurity involves a variety of policies, techniques, technologies, and procedures that work together to safeguard the availability, confidentiality, and integrity of computing resources, networks, software programs, and data from intrusion. Cybersecurity is the process of defending systems, networks, and programs from electronic (malicious) attacks. Machine learning (ML) is the process of creating models for the broad relationships among data sets after automatically analyzing massive data sets. Since the Pragmatism paradigm best exemplifies the harmony between knowledge and action, it was chosen as the research philosophy for this study. Focused group discussions served as the main study design while the qualitative component was predominantly used in the knowledge generating component, which was based on an integral research architecture that incorporates descriptive, narrative, theoretical, and experimental survey methodologies. The quantitative dimension investigated machine learning and cybersecurity prototype models using an experiment as a study design. The commercialization of priority projects for

strategic investment included post-harvest technologies, small-scale mining, mineral value addition, and bio-mining, clean water alternatives, mining waste-derived tile technologies, ICT innovations in machine learning and cybersecurity, and defense technologies. To direct the installation of upcoming cybersecurity systems in Africa, a Bayesian Network model for cybersecurity was developed. The research developed an effective network intrusion detection system using the KDDCup 1999 intrusion detection benchmark dataset. The sample included 494,020 instances of primary data with 42 variables that were analyzed mostly using the SNORT open source program and other Bayesian Network-supporting platforms. The most effective ML algorithms were considered while creating a Bayesian Network model.