School of Technology
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Item Detecting Data Exfiltration Anomalies in Academic Networks Using the Isolation Forest Algorithm(KCA University, 2025) Arusei, Mike K.; Dr. Njenga, StephenAcademic networks face increased risks of data exfiltration due to sensitive personal information and research data. Traditional supervised detection models rely on labeled datasets which are often unavailable in resource constrained institutions. This study investigates the applicability of the unsupervised Isolation Forest algorithm for detecting anomalous network traffic indicative of data exfiltration. The research utilized the CICIDS2017 dataset focusing on the Thursday-WorkingHours-Afternoon-Infiltration subset. Key features including Flow Duration, Total Fwd Packets, Flow Bytes/s, Flow IAT Mean, and Destination Port were preprocessed and normalized for modeling. The model achieved a precision of 1.00, recall of 0.99 and F1-score of 1.00 for anomalous traffic detection successfully identifying approximately 4.8% of flows as anomalous. Comparative analysis with previous methods, including supervised Random Forest and SVM demonstrated that Isolation Forest offers competitive accuracy with lower computational overhead and does not require labeled data. The findings highlight the algorithm’s suitability for academic network monitoring, providing an effective early warning mechanism while emphasizing the importance of threshold tuning to reduce false positives.Item A knowledge-as-a-service support framework for ambient learning in Kenya(13th IADIS International Conference Information Systems, 2020) Mburu, Lucy W.; Karanja, Richard; Nyaga, Simon M.Knowledge as a Service (KaaS) is a relatively new model, albeit one that is rapidly gaining popularity within cloud computing environments. Over the recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. This need stems from the knowledge that users will be able to access applications and the information therein on demand, without the restrictions that are usually imposed by time and space. The KaaS model terms knowledge as the understanding of information based on its relevance to a specific context and problem area, thus forming a valuable resource for the human decision-making process. As motivated by the global sustainable development goal of ensuring inclusive and equitable quality education to promote learning opportunities for all, this research has developed a framework that is hinged on KaaS and utilizes knowledge from ambient learning systems. The main aim is to provide a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve the quality of education on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge. The research outcomes will benefit knowledge consumers such as the developers of ambient learning systems.Item An empirical approach to mobile learning on mobile ad hoc networks(Institute of Electrical and Electronics Engineers (IEEE), 2020) Mwendia, Simon N.; Ichaba, Mutuma; Musau, FelixMobile Ad hoc Networks (MANETs) are made up of mobile nodes that are interconnected wirelessly, while topology changes as mobile nodes join and leave the network. MANETs do not depend on fixed infrastructure. Due to their dynamism and low cost (no infrastructure is needed), MANETs have been proposed as a mechanism suitable for carrying out mobile learning (m-Leaning) in developing countries. However, systematic literature review indicates that the existing MANETs-based m-Learning models are disadvantaged because they fail to identify possible routing protocols able to support such models. As a result, it becomes very difficult to implement the existing MANET-based m-Learning models. This paper characterizes MANETs-based m-Learning proposed by [1]. Thereafter, it uses area, nodes, and data packets information as basic scalar parameters on Zone Routing Protocol (ZRP) simulated on NS-2 and ZRP code supplemented with positional and directional information of nodes in the Intrazonal Routing Protocol (IARP) on OMNET++. According to simulation results, a directional-positional enhanced ZRP outperforms regular ZRP on packet delivery ratio, delay and overall data packet throughput. Results from the simulation suggests that a supplemented ZRP is a feasible routing protocol for supporting m-Learning in a typical university campus based on the identified basic scalar parameters and characterization of [1].Item Developing an e-learning theory for interaction and collaboration using grounded theory: a methodological approach(The Qualitative Report at NSUWorks, 2021) Kibuku, Rachael N.; Ochieng, Daniel O.; Wausi, Agnes N.Grounded Theory (GT) is becoming an increasingly prevalent research methodology in many fields. Although researchers use it in qualitative and quantitative studies, it is more popular with qualitative studies, as evidenced by the citations from previous research. This paper aims to document and present how we used GT in our qualitative research to construct an e-learning theory for interaction and collaboration. It also includes the justification of GT. We adopted and adapted the constructivist GT (CGT). Therefore, this paper discusses the CGT methodology, its philosophical, ontological and epistemological perspectives. It also includes the research design that captures how we sampled the participants, collected, analyzed and interpreted the data, and how we documented the research findings in the context of CGT. It also includes the justification of the decisions we made and the extent to which they align with CGT. Using CGT, we listened to, observed and captured e-learners’ and e-tutors’ stories and experiences which yielded rich and insightful data that informed the development of the e-learning theory for interaction and collaboration. We also present the challenges we experienced when using CGT and the strategies we used to overcome them. Finally, we have included the methodological insights we drew from using CGT in our research. This paper has presented the CGT design strategy; thus, it will be helpful, especially to novice and future researchers aspiring to use the methodology to conduct their research.Item E-learning challenges faced by universities in Kenya: a literature review(2020) Kibuku, Rachael N.; Ochieng, Daniel O.; Wausi, Agnes N.Some institutions of higher education in Kenya have adopted e-Learning with the aim of coping with the increased demand for university education and to widen access to university training and education. Though there are advantages that accrue from adopting e-Learning; its implementation and provision has not been smooth sailing. It has had to contend with certain national, organisational, technical and social challenges that undermine its successful implementation. This paper therefore aims to present a literature review of the challenges faced in the implementation and provision of e-Learning in universities in Kenya. The scoping review method was used to identify and analyze the literature of the e-Learning challenges. Some of the challenges revealed include: lack of adequate e-Learning policies, inadequate Information and Communication Technology (ICT) infrastructure, the ever evolving technologies, lack of technical and pedagogical competencies and training for e-tutors and e-learners, lack of an e-Learning theory to underpin the e-Learning practice, budgetary constraints and sustainability issues, negative perceptions towards e-Learning, quality issues, domination of e-Learning aims by technology and market forces and lack of collaboration among the e-Learning participants. These challenges need to be addressed to minimise their impact on implementation and delivery of e- Learning initiatives in institutions of higher education in Kenya. This analysis of the e-Learning challenges forms the basis for the ongoing research that seeks to explore and establish possible strategies to address some of these challenges.Item E-learners’ challenges and coping strategies in interactive and collaborative e-learning in Kenya(Journal of Education and Training Studies, 2020) Kibuku, Rachael N.; Ochieng, Daniel O.; Wausi, Agnes NSome universities in Kenya have taken up e-learning to flexibly deliver learning and bridge the educational access gap. Despite the benefits that e-learning offers to the e-learners, there are challenges that they must cope with. The aim of this paper is to present the challenges that emerged from research that was undertaken in two Institutions of Higher Learning (IHLs). It also presents the coping strategies that e-learners used to overcome the challenges. The research used the constructivist version of Grounded Theory (GT) methodology. It used in-depth interviews and participant observations to gather data from the e-learners, e-tutors, e-learning managers and e-learning platforms. Therefore, the research yielded qualitative data which was analyzed using Atlas.ti software. Data was analyzed thematically to establish the patterns of challenges and the equivalent coping strategies. The results are presented using the Gioia technique and the discussion used the vignettes technique from participants in order to preserve their voice. The challenges that emerged relate to: e-content, coursework, internet access, e-learning technology, Information and Communication Technology (ICT) skills and training, interaction and collaboration, personal issues, teaching of Science, Technology, Engineering and Mathematics (STEM) courses and the tutorials. Recommendations on how to tackle these challenges have also been suggested. An understanding of these challenges is important to the e-learning players so that they can adopt interventions to mitigate them and hence improve interaction and collaboration. The results presented in this paper are part of the larger research whose main objective was to develop an e-learning theory for interaction and collaboration.Item Further Construction Of Balanced Arrays(Pushpa Publishing House, 2020) Wanyoike, John N.; Njui, F.; Manene, M.The relation between balanced arrays and two other combinatorial structures, namely, orthogonal arrays and transitive arrays is pointed out. We provide three new and simple but rather stringent methods of constructing balanced arrays of any strength provided that the balanced arrays exist. A theorem that enables one to generate a balanced array from several known balanced arrays has been proved. The existence results of some types of balanced arrays based on the existence of some types of Hadamard matrices have also been proved.Item Applying Data Mining in Graduates’ Employability : A Systematic Literature Review(International Journal of Engineering Pedagogy, 2023) Mburu, Lucy W.; Mwendia, Simon N.; Mpia, Héritier N.Envisaging an adequate IT/IS solution that can mitigate the employability problems is imperative because nowadays there is a high rate of unemployed graduates. Thus, the main goal of this systematic literature review (SLR) was to explore the application of data mining techniques in modeling employability and see how those techniques have been applied and which factors/variables have been retained to be the most predictors or/and prescribers of employability. Data mining techniques have shown the ability to serve as decision support tools in predicting and even prescribing employability. The review determined and analyzed the machine learning algorithms used in data mining to either predict or prescribe employability. This review used the PRISMA method to determine which studies from the existing literature to include as items for this SLR. Hence, 20 relevant studies, 16 of which are predicting employability and 4 of which are prescribing employability. These studies were selected from reliable databases: ScienceDirect, Springer, Wiley, IEEE Xplore, and Taylor and Francis. According to the results of this study, various data mining techniques can be used to predict and/or to prescribe employability. Furthermore, the variables/factors that predict and prescribe employability vary by country and the type of prediction or prescription conducted research. Nevertheless, all previous studies have relied more on skill as the main factor that predict and/or prescribe employability in developed countries and none studies have been conducted in unstable developing countries. Therefore, the need to conduct research on predicting or prescribing employability in such countries by trying to use contextual factors beyond skill as features.Item Predicting Employability of Congolese Information Technology Graduates Using Contextual Factors: Towards Sustainable Employability(Sustainability Journal, 2022) Mwendia, Simon N.; Mburu, Lucy W.; Mpia, Héritier N.Predicting employability in an unstable developing country requires the use of contextual factors as predictors and a suitable machine learning model capable of generalization. This study has discovered that parental financial stability, sociopolitical, relationship, academic, and strategic factors are the factors that can contextually predict the employability of information technology (IT) graduates in the democratic republic of Congo (DRC). A deep stacking predictive model was constructed using five different multilayer perceptron (MLP) sub models. The deep stacking model measured good performance (80% accuracy, 0.81 precision, 0.80 recall, 0.77 f1-score). All the individual models could not reach these performances with all the evaluation metrics used. Therefore, deep stacking was revealed to be the most suitable method for building a generalizable model to predict employability of IT graduates in the DRC. The authors estimate that the discovery of these contextual factors that predict IT graduates’ employability will help the DRC and other similar governments to develop strategies that mitigate unemployment, an important milestone to achievement of target 8.6 of the sustainable development goals.