Saudi Cultural Missions Theses & Dissertations
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Item Restricted Securing Saudi Arabia’s Smart Cities and Critical Infrastructure Against APTs: A Framework for IoT/OT Forensic Readiness(Saudi Digital Library, 2025) Alarjani, Abdulaziz; Lutui, RaymondThe Vision 2030 of Saudi Arabia has encouraged the development of smart cities by means of all-inclusive integration of Internet of Things (IoT) and Operational Technology (OT) systems. While this transformation is very positive, it also makes critical national infrastructure more vulnerable to advanced cyber threats like Advanced Persistent Threats (APTs). This dissertation demonstrates that while the Kingdom is investing heavily in cybersecurity, there is a major gap in the area of forensic preparedness in these complex IoT/OT environments. The main problem is not only technical, but also related to major legal and procedural ambiguities in the applicable frameworks. This paper conducts a Multi-Vocal Literature Review (MVLR) of Saudi Arabia's Anti-Cyber Crime Law and Personal Data Protection Law (PDPL) to show how laws that are intended for conventional IT are causing challenges for investigators in obtaining digital evidence from Smart City systems. A comparative study of international frameworks, followed by a derived SWOT analysis, characterises a pressing demand for clarity of procedure on a jurisdictional basis. The paper concludes with four useful suggestions for how forensic preparedness practice may be enhanced by addressing these gaps in the law through mandatory 'forensics by design', standardised procedures, and capacity building of locally based expertise. This study contributes to a policy-focused approach to securing smart cities through the inclusion of legal and procedural considerations into the technical cybersecurity strategy for the Kingdom of Saudi Arabia.12 0Item Restricted FROM DISCLOSURE TO EXPLOITATION(Saudi Digital Library, 2025) Alsadi, Arwa Abdulkarim; Hernández, Gañán; van, EetenThe rapid growth of Internet-of-Things (IoT) devices, such as smart cameras, home routers, and smart thermostats, has transformed the digital landscape while also introducing new cybersecurity risks. IoT systems are often targeted by attackers due to outdated software, long device lifespans, and fragmented security practices. Although many IoT vulnerabilities are discovered and disclosed, only a small fraction are actually exploited in the wild. This raises important questions about which vulnerabilities are targeted, why attackers choose them, and how long they remain in use. This dissertation investigates how IoT vulnerabilities are selected for exploitation in practice, with a particular focus on attacker behavior, exploit development, and vulnerability characteristics. It systematically examines the interplay between these factors to understand how they collectively shape exploitation trends in IoT ecosystems. To answer the central research question on \textit{What factors shape the exploitation in IoT vulnerabilities, from target selection to exploit development and prediction?}, this dissertation presents four peer-reviewed studies. Chapter 2 provides a longitudinal analysis of over 17,000 IoT malware samples, revealing that only a handful of IoT vulnerabilities are targeted and often exploited for years after their disclosure. The average time-to-exploit a vulnerability after disclosure was found to be 29 months, far longer than in traditional IT systems. This temporal persistence highlights the enduring value of certain vulnerabilities within the attacker ecosystem. Chapter 3 examines factors influencing exploitation frequency in IoT vulnerabilities. It finds that attackers prefer vulnerabilities that are easy to exploit, affect widely deployed devices, and are difficult to patch. Technical severity scores, like CVSS, were less predictive than contextual factors such as device type and patch complexity. Chapter 4 addresses the limitations of existing prediction systems, such as the Exploit Prediction Scoring System (EPSS), in assessing IoT-specific risk. By incorporating attacker community discussions from underground forums into a new predictive model, the study significantly improves accuracy and highlights the importance of behavioral and vendor-related features in anticipating exploitation for IoT devices. Finally, Chapter 5 shifts focus to the human element through interviews with 16 Proof-of-Concept (PoC) exploit developers. It finds that disclosure decisions are shaped by individual motivations, ethical considerations, and vendor interactions. PoCs developers play a key role in making vulnerabilities exploitable and often act as gatekeepers in the vulnerability ecosystem. This qualitative study examines the socio-technical dynamics influencing PoC developers’ decisions to publish exploits, and how these choices can shape target selection and enable the weaponization of vulnerabilities. Collectively, these findings show that targeting in IoT is not random but follows strategic patterns driven by cost, opportunity, and long-term exploit value. The dissertation argues that current governance mechanisms—market incentives, disclosure systems, and risk models, are misaligned with real-world exploitation practices and therefore fall short in addressing the distinct dynamics of IoT security. To address these gaps, it proposes a hybrid governance model that combines regulatory oversight, community collaboration, and market-based tools to more effectively manage the lifecycle of IoT vulnerability and exploitation.3 0Item Restricted The Impact of Digital Transformation on Enhancing the Medical Supply Chain(Saudi Digital Library, 2025) Abdulaziz, Albaraa; Theo, FotisThe current thesis aims to investigate how digital transformation (DT) technologies, artificial intelligence (AI), the Internet of Things (IoT), and blockchain can be used to improve the medical supply chain in Saudi Arabia through the Vision 2030 metric. A systematic review based on PRISMA was used to identify and screen the studies (n=6); narrative and thematic synthesis were performed as part of the study, and three general themes were created: operational excellence, economic viability, and implementation readiness. According to the findings, DT enhances visibility, predictability, responsiveness, and resilience, but the strength of evidence was not consistent, and some of the studies have indicated a high implementation cost and organisational barriers. It is important to note that the synthesis has revealed the convergence as well as tensions among studies that technical interoperability and workforce readiness are important in the effective adoption of DT. Some of these recommendations are gradual execution of technology, capacity building initiatives and sponsorship of national policy to enable equal adoption in all the facilities. Its implications go beyond Saudi Arabia, and the fact that DT serves as an agent of predictive and agile systems and the global discussions about how digital health transformation strategies are to be implemented.6 0Item Restricted A Facial Expression-Aware Edge AI System For Driver Safety Monitoring(Saudi Digital Library, 2025) Almodhwahi, Maram; Wang, BinThis dissertation presents a driver monitoring system (DMS) that integrates emotion recognition to address critical issues in road safety. Road safety has become a global concern due to the significant increase in vehicle numbers and the rapid growth of transportation infrastructure. The number one cause of road accidents is human error, with a 90% ratio, with common contributing factors like distraction, drowsiness, panic, and fatigue. Traditional DMS approaches often fall short in identifying these emotional and cognitive states, limiting their effectiveness in accident prevention. To address these limitations, this research proposes a robust, deep-learning-based DMS framework designed to identify and respond to driver emotions and behaviors that may compromise safety. The proposed system utilizes advanced convolutional neural networks (CNN), specifically the inception module and Caffe-based ResNet-10 with a single-shot detector (SSD), to perform efficient facial detection and classification. These chosen model structures helped balance computational efficiency and accuracy. The DMS is trained on an extensive, diverse dataset comprising approximately 198,000 images and 1,600 videos sourced from multiple public and private datasets, ensuring the system’s robustness across a range of emotions and real-world driving scenarios. Emotions of interest include high-risk states such as drowsiness, distraction, and fear, alongside neutral conditions, and the model can perform well in different conditions, including low-light and foggy/blurry environments. Methodologically, the system incorporates essential data preprocessing techniques such as resizing, brightness normalization, pixel scaling, and noise reduction to optimize the model’s performance. On top of that, data augmentation and grayscale conversion improves the dataset’s variability, allowing the decrease of computational costs without sacrificing accuracy. This approach enabled the model to achieve high performance metrics, with an overall accuracy of 98.6% , an F1-score of 0.979, precision of 0.980, and recall of 0.979 across the four primary emotional states. This research contributes to the field by offering a less invasive, real-time solution for monitoring high-risk driver behaviors and providing insights for further advancements in automated driver assistance technologies. Future directions include optimizing the system for microcontrollers with low power consumption and implementing alerts for high-risk states to further mitigate accident risks, as well as including a multi-modal fusion of data from different sources (Infrared Camera, and a Microphone) to increase emotion recognition accuracy, which leads to taking better control and initiating more efficient proactive interventions.24 0Item Restricted THE IMPACT OF INDUSTRY 4.0 TECHNOLOGIES ON SUPPLY CHAIN RESILIENCE IN SAUDI ARABIA(Saudi Digital Library, 2025) Alanazi, Jawaher; Greasley, AndrewThis dissertation examines the impact of Industry 4.0 technologies on supply chain resilience in Saudi Arabia, where initiatives such as Internet of Things and the Global Supply Chain Resilience Initiative (GSCRI) are central to industrial transformation. A systematic literature review (SLR) guided by the PRISMA framework was conducted, covering academic and grey literature published between 2020 and 2025. The search initially yielded 315 records; after applying inclusion and exclusion criteria, 26 studies were selected for detailed analysis. The findings indicate thatInternet of Things (AI), blockchain, the Internet of Things (IoT), and digital twins are the leading technologies supporting resilience capabilities, particularly in risk mitigation, operational continuity, agility, and sustainability. However, barriers such as high implementation costs, limited absorptive capacity, institutional inertia, and fragmented infrastructure constrain their full potential. This research contributes by integrating insights from policy initiatives, industry practices, and recent empirical studies to show how Industry 4.0 adoption strengthens resilience in supply chains within an emerging economy context. It underscores the strategic role of digital transformation in enabling continuity, flexibility, and sustainability. The study concludes with practical recommendations for policymakers and practitioners in Saudi Arabia, emphasizing the need to strengthen digital infrastructure, enhance workforce skills, and foster cross-sector collaboration. It also highlights avenues for future research, including empirical studies on digital maturity and comparative analyses across sectors and regions.26 0Item Restricted Resource Scheduling Strategies to Optimise QoS in Integrated IoT and Fog Computing Environments(Saudi Digital Library, 2025) Alshammari, Naif Hamdan; Pervaiz, Haris; Ahmed, Hasan; Ni, QiangIn recent years, there has been a tremendous increase in the internet and its applications, which has attracted the attention of scholars and industries to investigate how to improve the quality of services (QoS). Improving QoS is considered a major challenge for users of IoT devices. To address this challenge, several technologies have emerged to extend cloud computing, including fog computing, which provides computational resources at the network’s edge. Nevertheless, fog computing faces limitations due to the constrained resources, limited computational processes and storage in fog devices compared to cloud infrastructure. This thesis investigates resource scheduling strategies to optimise QoS in fog computing, focusing on task scheduling and resource allocation approaches. The thesis begins with a qualitative comparative analysis of existing resource management approaches to optimise QOS. It classifies resource management approaches into several categories: application placement, task scheduling, resource allocation, task offloading, load balancing, and resource provisioning. These categories are either task-oriented, such as application placement, task scheduling, and task offloading, or resource-oriented, including resource allocation, load balancing, and resource provisioning. It also introduces a novel intelligent resource scheduling model using gated graph convolution neural networks (GGCNs) to trade off between delay and network usage with a limited number of fog nodes. The GGCN model outperforms various other existing approaches like PSO, FCFS, and JSF by 86.09%, 98.53%, and 98.02% respectively, in terms of total network usage. Additionally, in terms of loop delay, it achieves improvements of 68.64% over PSO, 92.07% over FCFS, and 76.26% over SJF. Furthermore, it presents a novel multi-objective scheduling framework utilising an enhanced multi-layer perceptron (eMLP). This new mechanism optimises several parameters, including delay, power consumption, and cost, while simultaneously optimising bandwidth. Experimental results show that eMLP reduces delay, network usage and cost by 75%, 65%, and %70 respectively, compared to other benchmark schemes such as GNN, SMA, FCFS, and SJF. Finally, the thesis discusses the current gaps and future directions for enhancing and further investigating QoS through fog computing.15 0Item Restricted Novel Framework for Integrating Blockchain Technology into Logistics and Supply Chain Services(Saudi Digital Library, 2025) Alkhaldi, Bidah; Al-Omary, AlauddinBottlenecks and operational inefficiencies in supply chains still persist despite technological innovations, due to structural and managerial issues. Blockchain integration presents a viable solution to these long-standing issues by offering tamper resistant ledgers, secure transactions and automation capabilities. This research takes a novel approach by designing a blockchain integration framework for supply chains, modifying the MOHBSChain framework to create the Supply-Blockchain framework. This framework is validated by developing a functional prototype using Hyperledger Fabric, by considering a port decongestion use case scenario. This research adopted an inductive approach, starting with informal observations of real-world port operations and a targeted literature review to identify patterns and challenges. The framework development was guided by the principles of transaction cost economics, resource-based view, and diffusion of innovations theories. MoSCoW method was used to prioritize features, while agile project management was adopted to ensure timely completion. Hyperledger Firefly and its connector framework were used as the middleware to facilitate blockchain integration, while chaincode developed using Go language was packaged and deployed to implement smart contracts. Raft orderer consensus mechanism was chosen to ensure resilience and fault tolerance. From a core functionality standpoint, the prototype allows initiation of smart contracts corresponding to functions such as creating and editing supply chain process-related documents, minimizing manual interventions and enhancing efficiency to reduce port congestion. It also offers live tracking of blockchain transactions, facilitating transparency and oversight, the permissioned nature of Hyperledger Fabric ensures security and robust access controls. Results of functional and performance testing conducted using Hyperledger Caliper, Prometheus, and Grafana, were satisfactory; this indicates the prototype's potential in alleviating bottlenecks in supply chains and quickly delivering benefits to key stakeholders such as port authorities, customs officers, shipping line representatives and logistics providers. In terms of limitations, the prototype is limited to basic functionalities and lacks advanced features required to meet operational and regulatory standards. Future improvements can focus on integrating AI for tasks such as predictive analytics and automated document verification, while technologies such as NFT-based schemas can enhance ownership verification and improve asset tracking.52 0Item Restricted Enhancing Robustness and Energy Efficiency of IoT-Coupled Machine Learning Applications(University of Georgia, 2025) AlShehri, Yousef; Ramaswamy, LakshmishMachine learning (ML) applications continue to revolutionize many domains. In recent years, there has been considerable research interest in building novel ML applications for a variety of Internet of Things (IoT) domains, such as precision agriculture, smart cities, and smart manufacturing. However, IoT characteristics pose several fundamental challenges to designing and implementing effective ML applications at the edge, specifically, inference-time data incompleteness and quality induced by sensor failures and the energy management of such applications. This dissertation comprises four components aimed at addressing the aforementioned challenges to enhance IoT ML-based systems’ robustness, energy efficiency, and reliability against sensor failures at inference time. First, to tackle the sudden and concurrent missing data of sensors at the inference time of the ML application, it introduces an ensemble of a few sub-models, each trained with distinct subsets of sensors chosen based on correlation to make ML robust against missing data. This ensemble-based approach maintains the accuracy of the IoTMLapplication during the occurrence of missing data at inference time via performing predictions using sub-model(s) that are built using the correlated sensors to the faulty sensors, excluding the faulty sensors. The second component involves an extensive study to identify energy bottlenecks for operating our ensemble-based approach, SECOE, on a resource-constrained edge device and find the number of ensemble models that further maintain the ML application’s performance while optimizing energy consumption. The third component introduces an energy-aware ensemble of models with enhanced energy management capabilities for handling data incompleteness during inference time to fit resource-constrained IoT devices. Finally, our research offers an innovative autoencoder model for correcting inaccurate/faulty sensor readings caused by the malfunctioning of multiple sensors simultaneously at inference time. This model treats different faulty readings (e.g., drift and bias) as one type via a correlation-based masking mechanism. The experimental results have demonstrated that our methods have effectively alleviated the impact of IoT sensor failures on the performance of theMLapplication during the inference time while optimizing its energy consumption, thereby decisively outperforming existing methods and significantly improving the overall reliability of ML coupled IoT applications.22 0Item Restricted OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES(Florida Atlantic University, 2025-03-11) Al Hanif, Abdulelah; Ilyas, MohammadThe rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic.38 0Item Restricted Design, Development and Deployment of Customisable Mobile and IoT Systems to Enhance Mosquito Surveillance(University College London, 2025-04-10) Aldosery, Aisha; Kostkova, PattyMosquito-borne diseases pose significant public health challenges in tropical and subtropical regions, requiring precise and efficient surveillance methods. Traditional field data collection methods often lack the accuracy, timeliness and efficiency required to control outbreaks. This research advances mosquito field surveillance by designing, developing, and deploying two digital solutions customised for distinct environmental settings in Northeast Brazil and Madeira Island: a Mobile Surveillance System and an Internet of Things (IoT) Environmental Monitoring System. Developed through iterative processes and stakeholder engagement, these adaptable and scalable systems enhance the accuracy and granularity of data collection. The Mobile Surveillance System was adapted to regional requirements: in Brazil, it comprises a mobile app for field agents and a web platform for supervisors; in Madeira, it combines both functionalities into a single, unified mobile app. The implementation of both applications shares a common architecture that not only proves the systems’ generalisability but also boosts operational efficiency and data accuracy by digitising field data, supporting field agents, and aiding supervisors in managing activities. The IoT-based Environmental Monitoring System, featuring a five-layer architecture, including Arduino microcontrollers, weather and water sensors, and a water pump, autonomously and continuously captures high-resolution data, offering deeper insights into environmental influences on mosquito populations and supporting precise, location-specific predictive modelling. Detailed statistical analyses using Bayesian hierarchical models and correlation studies were conducted to pinpoint critical environmental predictors of mosquito breeding. Furthermore, a neural network classification-based predictive model was developed, enhancing weekly mosquito presence predictions by analysing temporal and sequential environmental patterns. This research distinguishes itself through its real-world deployments, addressing infrastructure, logistics, and technology challenges, while underscoring the importance of a process-oriented approach in tool development and recommending longitudinal deployments to assess the long-term impact of these technologies on mosquito population control and disease management in diverse environments.18 0
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