Saudi Cultural Missions Theses & Dissertations
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Item Restricted A RISK-ADAPTIVE ACCESS CONTROL MODEL FOR THE SERVICE MESH IN A MICROSERVICES ARCHITECTURE(The University of Tulsa, 2025-05) Alboqmi, Rami; Gamble, RoseCloud computing has transformed our lives by enabling applications to be deployed at scale, allowing a broad range of customers to access services seamlessly. However, as cloud computing has evolved, several challenges have emerged, such as meeting high customer demands while maintaining system stability and scalability. As a result, the cloud community introduced cloud-native computing in 2015, enabling applications to be scaled efficiently to meet customers’ demands. The microservices architecture (MSA) is a key enabler of cloud-native application development. It allows developers to build an application's components loosely and independently as microservices (also referred to as services). Following and applying the MSA architecture has many benefits, such as a failure within a microservice may not affect the entire deployed MSA application. For example, a failure in the temperature display microservice functionality does not affect the core functionalities of other microservices, such as map navigation. The map navigation microservice will still operate without temperature data. As a result, an MSA application becomes more resilient to failure. However, MSA introduces challenges in securing communication between microservices where orchestration solutions cannot ensure secure communications. A rogue microservice could act as a backdoor, compromising other microservices within the MSA application after initial authentication and authorization at deployment. Thus, service mesh technology was introduced as an infrastructure layer within an orchestration solution in 2017 to handle robust security, such as secure microservices-to-microservices communication with features like mutual TLS. Nevertheless, the current service mesh solutions are not mature yet and still rely on static AC policies set at deployment. In addition, these static policies operate with implicit trust between microservices, which do not adapt to changes in response to the trustworthiness of microservice. As a result, the service mesh limits its ability to detect compromised microservices at runtime, requires manual AC policy updates, and creates security gaps. A dynamic AC model for the service mesh is crucial to continuously assess the trustworthiness of microservices based on their behavior and vulnerability posture to align with the Zero Trust (ZT) principle of “never trust, always verify.” Additionally, any proposed dynamic AC model for the service mesh must not only offer dynamic and adaptive AC policies but also address the research gap in service mesh in the lack of capabilities such as sharing threat intelligence and enforcing automated microservice owner compliance requirements at runtime. These capabilities are essential for continuous monitoring and adaptive security responses for MSA applications at runtime. To dynamically adjust AC policies at runtime based on the trustworthiness of microservices, this research introduces the Service Mesh risk-Adaptive Access Control (SMAAC). SMAAC consists of three components: (1) Runtime Trust Evaluator (RTE) that assigns a trust metric (TM) to all microservices based on their behaviors and vulnerabilities; (2) Threat Intelligence Sharing (TIS) that shares TM values and vulnerability reports of all microservices; and (3) Access Policy Generation (APG) that creates dynamic AC policies when the TM of a microservice falls below a compliant threshold. Evaluated on three research MSA applications μBench, Lakeside Mutual, and Train Ticket, SMAAC effectively shows an adaptive mechanism for creating compliant AC policies to secure the operations of microservices and reduce security risks.15 0Item Restricted Reducing Type 1 Childhood Diabetes in Saudi Arabia by Identifying and Modelling Its Key Performance Indicators(Royal Melbourne Institute of Technology, 2024-06) Alazwari, Ahood; Johnstone, Alice; Abdollahain, Mali; Tafakori, LalehThe increasing incidence of type 1 diabetes (T1D) in children is a growing global health concern. Reducing the incidence of diabetes generally is one of the goals in the World Health Organisation’s (WHO) 2030 Agenda for Sustainable Development Goals. With an incidence rate of 31.4 cases per 100,000 children and an estimated 3,800 new cases per year, Saudi Arabia is ranked 8th in the world for number of T1D cases and 5th for incidence rate. Despite the remarkable increase in the incidence of childhood T1D in Saudi Arabia, there is a lack of meticulously carried out research on T1D in children when compared with developed countries. In addition, it is crucial to recognise the critical gaps in current understanding of diabetes in children, adolescents, and young adults, with recent research indicates significant global and sub-national variations in disease incidence. Better knowledge of the development of T1D in children and its associated factors would aid medical practitioners in developing intervention plans to prevent complications and address the incidence of T1D. This study employed statistical, machine learning and classification approaches to analyse and model different aspects of childhood T1D using local case and control data. In this study, secondary data from 1,142 individual medical records (359-377 cases and 765 controls) collected from three cities located in different regions of Saudi Arabia have been used in the analysis to represent the country’s diverse population. Case and control data matched by birth year, gender and location were used to control confounders and create a more robust and clinically relevant model. It is well documented that genetic and environmental factors contribute to childhood T1D so a wide range of potential key performance indicators (KPIs) from the literature were included in this study. The collected data included information on socioeconomic status, potential genetic and environmental factors, and demographic data such as city of residence, gender and birth year. Several techniques, such as cross-validation, hyperparameter tuning and bootstrapping, were used in this study to develop models. Common statistical metrics (coefficient of determination, R-squared, root mean squared error, mean absolute error) were used to evaluate performance for the regression models while for the classification models accuracy, sensitivity, precision, F score and area under the curve were utilised as performance measures. Multiple linear regression (MLR), artificial neural network (ANN) and random forest (RF) models were developed to predict the age at onset of T1D for all children 0-14 years old, as well as for the most common age group for onset, the 5-9 year olds. To improve the performance of the MLR models, interactions between variables were considered. Additionally, risk factors associated with the age at onset of T1D were identified. The results showed that MLR and RF outperformed ANN. The logarithm of age at onset was the most suitable dependent variable. RF outperformed the others for the 5-9 years age group. Birth weight, current weight and current height influenced the age at onset in both age groups. However, preterm birth was significant only in the 0-14 years cohort, while consanguineous parents and gender were significant in the 5-9 age group. Logistic regression (LR), random forest (RF), support vector machine (SVM), Naive Bayes (NB) and artificial neural network (ANN) models were utilised with case and control data to model the development of childhood T1D and to identify its key performance indicators. Full and reduced models were developed to determine the best model. The reduced models were built using the significant factors identified by the individual full model. The study found that full LR had the highest accuracy. Full RF and SVM with a linear kernel also performed well. Significant risk factors identified as being associated with developing childhood T1D include early exposure to cow’s milk, high birth weight, positive family history of T1D and maternal age over 25 years. Poisson regression (PR), RF, SVM and K-nearest neighbor (KNN) were then used to model the incidence of childhood T1D, taking in the identified significant risk factors. The interactions between variables were also considered to enhance the performance of the models. Both full and reduced models were created and compared to find the best models with the minimum number of variables. The full Poisson regression and machine learning models outperformed all other models, but reduced models with a combination of only two out of three independent variables (early exposure to cow’s milk, high birth weight and maternal age over 25 years) also performed relatively well. This study also deployed optimisation procedures with the reduced incidence models to develop upper and lower yearly profile limits for childhood T1D incidence to achieve the United Nations (UN) and Saudi recommended levels of 264 and 339 cases by 2030. The profile limits for childhood T1D then allowed us to model optimal yearly values for the number of children weighing more than 3.5kg at birth, the number of deliveries by older mothers and the number of children introduced early to cow’s milk. The results presented in this thesis will guide healthcare providers to collect data to monitor the most influential KPIs. This would enable the initiation of suitable intervention strategies to reduce the disease burden and potentially slow the incidence rate of childhood T1D in Saudi Arabia. The research outcomes lead to recommendations to establish early intervention strategies, such as educational campaigns and healthy lifestyle programs for mothers along with child health mentoring during and after pregnancy to reduce the incidence of childhood T1D. This thesis has contributed to new knowledge on childhood T1D in Saudi Arabia by: * developing a predictive model for age at onset of childhood T1D using statistical and machine learning models. * predicting the development of T1D in children using matched case-control data and identifying its KPIs using statistical and machine learning approaches. * modeling the incidence of childhood T1D using its associated significant KPIs. * developing three optimal profile limits for monitoring the yearly incidence of childhood T1D and its associated significant KPIs. * providing a list of recommendations to establish early intervention strategies to reduce the incidence of childhood T1D.34 0Item Restricted Assessment of measuring and monitoring safety in healthcare in Ireland and Saudi Arabia(University of Galway, 2023-08-16) Kaud, Yazeed; O'Connor, Paul; Lydon, SinéadIn the past two decades, governments worldwide have prioritised the improvement of health care quality and safety as a primary policy goal. However, progress towards achieving this goal has been limited. The provision of poor care and the prevalence of high levels of harm persist in hospitals. Measurement is considered a crucial and essential initial step in the process of improving patient safety. However, while measuring and monitoring patient safety is widely recognised, there is a lack of agreement on how to achieve it. In this thesis, a multi-method approach was taken to address the dimensions in the Measuring and Monitoring Safety (MMS) framework developed by Vincent et al. to provide a structure for understanding how safety is measured and monitored in hospitals in Ireland and Saudi Arabia. Study 1 is a scoping review of patient safety research carried out in the Republic of Ireland (RoI). It examines the extent, range, and nature of patient safety research activities carried out in the RoI; makes recommendations for future research; and considers how these recommendations align with the Health Service Executive’s (HSE) patient safety strategy. Study 2 considers how safety is measured and monitored in Irish hospitals and offers recommendations for how it can be improved. Study 3 is a scoping review that maps the quantity and nature of current patient safety research in Saudi Arabian hospitals, as well as identifies gaps in the existing literature. Finally, study 4 assesses the Saudi Arabian healthcare safety surveillance system in hospitals using the MMS framework. The findings from the four studies conducted in this thesis demonstrate the availability of an adequate amount of safety data, the availability of diverse methods for collecting safety data, and the expertise necessary to improve safety. The challenge is to identify the most efficient methods for generating key and high-quality data that can help multidisciplinary teams in developing effective interventions tailored to specific health contexts.26 0Item Restricted A VALUE-BASED MODELING FRAMEWORK FOR SOLAR ENERGY UTILIZATION AND MONITORING(Saudi Digital Library, 2023-12-08) Alanizi, Muslat Abdulrahman; Jololian, LeonWe have developed and presented a value-based modeling (VBM) framework for optimal solar energy utilization and monitoring. Our model adopts a universal approach that prioritizes values to ensuring a comprehensive analysis of solar energy systems by recognizing the complexities and intricacies of the renewable energy landscape. To determine the robustness and applicability of our VBM framework, we subjected it to a real-world test through a detailed case study focusing on Net-Metering Monitoring System. This validation reinforced the model's efficacy and showcased its potential as a dynamic tool for decision-making in solar energy. Using Shannon's entropy method, we recorded the optimal efficiency in solar power usage of the case study. These results, in terms of entropy values, highlight the stable and efficient use of solar energy after the implication of our value-based modeling framework. Additionally, our model has proven highly predictive, delivering accurate forecasts for net-metering values. Such predictive accuracy emphasizes the model's potential to assist utility providers, policymakers, and consumers make informed decisions about solar energy utilization. Hence, we introduce a pioneering Value-based Modeling framework for solar energy and highlight its practical significance and potential impact in optimizing and monitoring solar energy systems. Ultimately, we encouraged a sustainable and value-driven energy future. Keywords: Value-based Modeling, Solar Energy, Monitoring, Enterprise Systems, Net-metering, Process Improvement31 0