Oriental Journal of Computer Science and Technology

Gabriel Kabanda ¹

Affiliations
1 Atlantic International University 900 Fort Street Mall 40 Honolulu, Hawaii 96813, US

Abstract

Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

Keywords

Assignment Decisions, Big Data, Communication Methodology, Project Manager.

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Gabriel Kabanda ¹

Affiliations
1 Atlantic International University 900 Fort Street Mall 40 Honolulu, Hawaii 96813, US

Abstract

The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM).

Keywords

Autonomous Robotic Vehicle, Artificial Neural Networks, Bayesian Network, Cybersecurity, Decision Tree C4.5, Fuzzy Logic, Machine Learning Methods, Random Forests and Support Vector Machines (Svm).

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Gabriel Kabanda ¹

Affiliations
1 Atlantic International University, 900 Fort Street Mall 40, Honolulu, Hawaii 96813, US

Abstract

Cybersecurity systems are required at the application, network, host, and data levels. The research is purposed to evaluate Artificial Intelligence paradigms for use in network detection and prevention systems. This is purposed to develop a Cybersecurity system that uses artificial intelligence paradigms and can handle a high degree of complexity. The Pragmatism paradigm is elaborately associated with the Mixed Method Research (MMR), and is the research philosophy used in this research. Pragmatism recognizes the full rationale of the congruence between knowledge and action. The Pragmatic paradigm advocates a relational epistemology, a non-singular reality ontology, a mixed methods methodology, and a value-laden axiology. A qualitative approach where Focus Group discussions were held was used. The Artificial Intelligence paradigms evaluated include machine learning methods, autonomous robotic vehicle, artificial neural networks, and fuzzy logic. A discussion was held on the performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms.

Keywords

Artificial Intelligence, Artificial Neural Networks, Bayesian Network, Cybersecurity, Deep Learning, Machine Learning.

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Gabriel Kabanda ¹, Tinashe Chingoriwo ²

Affiliations
1 Zimbabwe Academy of Sciences, Trep Building, University of Zimbabwe Harare, ZW
2 Zimbabwe Open University, Corner House, Samora Machel Avenue/ L.takawira Street Harare

Abstract

Cybersecurity is a combination of technologies, processes and operations that are designed to protect information systems, computers, devices, programs, data and networks from internal or external threats, harm, damage, attacks or unauthorized access.1 The research was purposed to develop a cybersecurity culture framework which ensures that grassroot users of cyberspace are secured from cyber threats. Literature review showed that in Zimbabwe, no research had attempted to come up with a cybersecurity culture framework for grassroot users of cyberspace.The research was guided by the interpretivist paradigm and employed a qualitative methodology. A descriptive research design was used to answer the research questions and unstructured interviews were done to ascertain the cybersecurity needs and challenges of grassroot users of cyberspace. A cybersecurity culture framework was then crafted based on the research findings. The researchers recommended that Zimbabwe should have a cybersecurity vision and strategy that cascades to the grassroot users of cyberspace. Furthermore, the education curricula should be revised so that it incorporates cybersecurity courses at primary and secondary school level. This will then ensure that ICT adoption is matched with cyber hygiene and responsible use of cyberspace.

Keywords

Artificial Intelligence; Big Cloud Computing; Culture Framework; Cybersecurity, Data Analytics, Internet Of Things; Machine Learning

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Gabriel Kabanda ¹, Collector Tendeukai Chipfumbu ², Tinashe Chingoriwo ³

Affiliations
1 Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Masrketing Sciences Midlands State University Faculty of Business Sciences, ZW
3 Faculty of Technology, Zimbabwe Open University, ZW

Abstract

Reinforcement learning (RL) is a type of ML, which involves learning from interactions with the environment to accomplish certain long-term objectives connected to the environmental condition. RL takes place when action sequences, observations, and rewards are used as inputs, and is hypothesis-based and goal-oriented. The key asynchronous RL algorithms are Asynchronous one-step Q learning, Asynchronous one-step SARSA, Asynchronous n-step Q-learning and Asynchronous Advantage Actor-Critic (A3C). The paper ascertains the Reinforcement Learning (RL) paradigm for cybersecurity education and training. The research was conducted using a largely positivism research philosophy, which focuses on quantitative approaches of determining the RL paradigm for cybersecurity education and training. The research design was an experiment that focused on implementing the RL Q-Learning and A3C algorithms using Python. The Asynchronous Advantage Actor-Critic (A3C) Algorithm is much faster, simpler, and scores higher on Deep Reinforcement Learning task. The research was descriptive, exploratory and explanatory in nature. A survey was conducted on the cybersecurity education and training as exemplified by Zimbabwean commercial banks. The study population encompassed employees and customers from five commercial banks in Zimbabwe, where the sample size was 370. Deep reinforcement learning (DRL) has been used to address a variety of issues in the Internet of Things. DRL heavily utilizes A3C algorithm with some Q-Learning, and this can be used to fight against intrusions into host computers or networks and fake data in IoT devices.

Keywords

Artificial Intelligence, Cybersecurity, Deep Learning, DRL Applications, E-Learning, Machine Learning, Reinforcement Learning.

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