Journal Articles
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Item Deep Learning Network Intrusion Detection with the Conv1d-Lstm Model: Integrating CNN and LSTM For Superior Performance(International Journal of Professional Practice (IJPP), 2024) Lukogo, Cikambasi Ciza; Muriira, Lawrence Mwenda; Murungi, Robert MutuaIncreased cases of cyber-attack and the rising levels of sophistication presents a significant threat to corporate networks, resulting in potential data breaches, financial losses, and reputational harm. Traditional Intrusion Detection Systems, which rely on predefined signatures and rules, have proven inadequate due to high false positive and false negative rates. This study introduces an innovative AI-based intrusion detection model to enhance corporate network security leveraging on deep learning techniques. The objective was to propose a Conv1d-LSTM Model, integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze network traffic data from the CSE-CIC-IDS-2018 dataset, which encompasses a wide array of attack types, and provides a realistic representation of modern network traffic. This deep learning model effectively detects complex patterns and temporal dependencies in the data. The performance of the innovated model was evaluated using precision, accuracy, recall, and F1 score, to demonstrate its superior detection capabilities compared to conventional Intrusion Detection Systems (IDS). Additionally, a comparative analysis of CNN and RNN performance on the same dataset was conducted, highlighting the strengths and limitations of each approach. This research underscores the importance of integrating advanced AI methodologies into IDS frameworks to protect corporate networks from cyber threats.Item Factors Determining Successful Adoption of Internet of Things (IoT) in University Libraries: A case of Catholic University of Eastern Africa Library(International Journal of Professional Practice, 2023-08) Maina, John; Gichohi, Paul Maku; Muriira, Lawrence MwendaThe potential of Internet of Things technology to enhance operational efficiency and effectiveness has been recognized across various sectors. However, its adoption in Kenyan university libraries remains limited. The primary aim of this study was to examine the factors that contribute to the successful adoption of Internet of Things (IoT) technology at the Catholic University of Eastern Africa library. The ultimate goal was to identify these factors and provide recommendations for best practices in implementing IoT in the library setting. The Unified Theory of Technology Acceptance and Use served as the guiding framework. Utilizing a descriptive survey research design, this study focused on a target population of seven section head librarians and five hundred library patrons. Employing a qualitative approach, data was collected through interviews with a purposively selected university librarian, five section head librarians, and six focus group discussions (7 each), involving simple randomly sampled library patrons. Document analysis was also conducted. Content analysis and thematic categorization were used for data analysis. The study found that IoT adoption in CUEA libraries is limited by infrastructure challenges, training gaps, and organizational barriers. Robust infrastructure, user acceptance, staff training, and organizational support are key success factors for IoT adoption. The study recommends that CUEA libraries should partner with university departments to allocate resources for IoT implementation and establish training programs to enhance staff skills. The university administration should support libraries by removing organizational barriers, facilitating budgets, and creating a conducive environment for seamless IoT integration. Implementing these recommendations will enhance knowledge management capabilities and contribute to research and learning environments. This study's novelty lies in its focus on university libraries and its actionable recommendations for improving operations and services through IoT technology.