Saudi Cultural Missions Theses & Dissertations
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Item Restricted Scalable Network Fingerprinting for IoT Devices(University of Southampton, 2024) Alyahya ,Tadani Nasser; Aniello, Leonardo; Sassone, VladimiroRecognising IoT devices through network fingerprinting contributes to enhancing the security of IoT networks and supporting forensic activities. Network fingerprinting for IoT devices involves analysing the traffic from these devices to accurately identify them without relying on explicit identifiers within the transmitted packets, which can be spoofed. Machine learning techniques have been extensively utilised in the literature to optimise IoT fingerprinting accuracy. Given the rapid proliferation of new IoT devices, a current challenge in this field is around how to make IoT fingerprinting scalable, which involves efficiently updating the used machine learning model to enable the recognition of new IoT devices. Some approaches have been proposed to achieve scalability, but they all suffer from limitations like large memory requirements to store training data and accuracy decrease for older devices. In this research, we propose a novel, scalable network fingerprinting method for IoT devices that leverages online stream learning and fixed-size session payloads. This approach enables the model to be updated periodically without needing to retain data, ensuring scalability and maintaining high recognition accuracy. Moreover, our method includes a mechanism for detecting unknown IoT devices. Our contributions are multifaceted, beginning with a comprehensive survey of passive IoT device fingerprinting that leverages machine learning and network characteristics, systematically reviewing the literature and detailing the network traffic features used for device identification. We identify key open research problems and future directions in this domain, highlighting significant challenges and gaps. A notable advancement is the introduction of ScaNeF-IoT, a scalable IoT fingerprinting approach utilising online stream learning and fixed-size traffic payload sessions, demonstrating high accuracy and adaptability. The scalability of the approach lies in its ability to continuously update the machine learning model with minimal resource overhead, allowing for the seamless recognition of new IoT devices without retraining from scratch. We further investigate the feature extraction method, which indicates the instances of interest from network traffic, such as packets, flows, or sessions, for further analysis and feature extraction, finding that fixed-size payload sessions outperform others with an accuracy of over 99.5% and an average false positive rate of 2.25%. Additionally, our scalable system is able to detect unknown IoT devices using online stream learning and z-score analysis, showcasing efficiency and adaptability. Our scalable IoT device fingerprinting approach achieves 100% accuracy in detecting unknown devices and 94% average accuracy in identifying known devices in streaming data.11 0Item Restricted Integrating Digital Technologies with Customer Relationship Management (CRM) to Enhance Customer Satisfaction and Loyalty in Luxury Hotels(Manchester metropolitan university, 2024) Assiri, Tarek; Cosser, GillianThis study investigates the integration of digital technologies—namely Artificial Intelligence (AI), Internet of Things (IoT), and Big Data analytics—into Customer Relationship Management (CRM) systems in luxury hotels. The research evaluates the impact of these technologies on customer satisfaction and loyalty through a quantitative approach, utilizing data from surveys conducted with hotel front-office employees. Findings reveal a varied adoption of digital tools, with IoT significantly enhancing operational efficiency, Big Data analytics improving customer retention strategies, and AI demonstrating underutilization due to staff training challenges. The study underscores the importance of aligning technology adoption with employee proficiency and guest expectations to optimize CRM effectiveness. Strategic recommendations include enhanced staff training programs, expansion of IoT applications, and leveraging Big Data for predictive analytics to strengthen customer relationships in the luxury hospitality sector. Limitations, such as the focus on luxury hotels and the exclusion of guest perspectives, highlight areas for future research15 0Item Restricted A PUF-based Keyless Authentication Paradigm for Secure IoT Systems(University of Louisiana at Lafayette, 2024) Alahmadi, Sara; Bayoumi, MagdyThe Internet of Things (IoT) drives innovation at individual and industrial scales, introducing massive interconnecting devices with varying security requirements. Authenticating these devices has emerged as a critical challenge, especially for constrained devices. In this context, Physically Unclonable Functions (PUFs) have gained popularity as promising hardware security primitives that offer lightweight and efficient solutions. Despite PUFs’ potential, they are susceptible to modeling attacks, leading researchers to explore new design approaches to increase their resiliency. This research addresses these challenges by developing different Arbiter PUF (APUF) solutions applicable to various applications from constrained devices to those requiring high security and post-quantum protection. First, a taxonomy of consumer IoT ( CIoT) and industrial IoT (IIoT) was presented to identify their distinguishing aspects. Addressing IoT security effectively requires considering the specific needs of different types of IoT applications, mainly consumer and industrial IoT. Second, a detailed analysis of APUF-based designs was conducted, measuring each design’s security scalability. This work evaluates the area and security of studied designs and defines an efficiency metric as security gain per area. Therefore, it showcases how the security of each of the studied design approaches scales in terms of area versus security, providing a guideline and insight for developers and for future improvement. Third, obfuscating techniques were introduced to secure APUF against modeling attacks. The methods implement transformation functions to obscure and safeguard the responses from modeling attacks. The first technique incorporates weak PUFs to fortify strong PUFs. The second technique encodes the challenges into constant weight vectors before generating the response. In addition, Dynamic Feedforward PUF was introduced to enhance the original Feedforward PUF. The method has two levels of configuration and incorporates randomness in the response generation process. Finally, a post-quantum PUF-driven authentication and message exchange framework (McPQ-PUF) was developed. This hybrid authentication and secret message exchange scheme utilizes two security primitives: APUF and McEliece, a post-quantum resilient Public Key Encryption (PKE). The McPQ-PUF framework is resilient against modeling and quantum attacks. This dissertation’s contribution should facilitate PUF-based authentication in an IoT environment. It provides secure and efficient solutions that address IoT ecosystems’ diverse security needs.9 0Item Restricted SMART INFRASTRUCTURE AND PARKING CITATION REVENUE IN THE PUBLIC SECTOR: THE ROLE OF SOCIOECONOMIC AND URBAN ENVIRONMENT FACTORS(University of Colorado Denver, 2024) Alharbi, Ahmad; Gregg, Dawn; Dincelli, ErsinThis dissertation investigates the impact of IoT projects on parking citation revenue (PCR) generation in the public sector, focusing specifically on smart parking systems (SPS). The research uses two empirical case studies to understand how SPS affects PCR and how various moderators, how socioeconomic status (SES) and urban environment factors, such as business vitality (BV), population density (PD), and amenity per capita (APC), influence this relationship. Study 1 examines the moderating effect of SES on the relationship between SPS and PCR. Grounded in the Transaction Cost Economics (TCE) framework and digital divide literature, it hypothesizes that higher SES areas, characterized by better access to technology and higher digital literacy, would experience a weakened positive relationship between SPS and PCR due to improved compliance and fewer violations. Utilizing a longitudinal dataset of 263,578 parking citations from Los Angeles (2015–2023) and employing a random-effects (RE) model, the findings confirm that in higher SES districts, the positive impact of SPS on PCR is diminished. Conversely, SPS leads to increased PCR in lower SES areas, highlighting the importance of addressing the digital divide to ensure equitable benefits from smart city initiatives. Furthermore, applying time limit (TL) policies within the SPS yields differential outcomes depending on the durations established. Specifically, shorter time limits are associated with increased PCR, while longer time limits correspond to decreased PCR. This underscores the influence of TL policies on parking behavior and compliance rates. Study 2 uses the Technology-Organization-Environment (TOE) framework to investigate how urban environment factors, BV, PD, and APC, moderate the SPS-PCR relationship. The hypotheses suggest that BV, PD, and APC would strengthen the positive impact of SPS on PCR due to higher parking demand. The results reveal that PD positively moderates the SPS-PCR relationship, supporting the hypothesis. However, contrary to expectations, higher BV and APC weaken the positive impact of SPS on PCR, possibly due to the availability of private parking options and policies prioritizing accessibility over strict enforcement in amenity-rich areas. The dissertation highlights the influence of socioeconomic and urban environmental contexts on technology effectiveness, using TCE and TOE frameworks. Practically, it provides insights for policymakers and urban planners, emphasizing the need for context-sensitive strategies in implementing IoT technologies to optimize benefits, address the digital divide, and achieve equitable, efficient, and sustainable urban development.21 0Item Restricted A Security Risk Assessment Framework for IoT Systems(University of Regina, 2024-08) Waqdan, Mofareh Abdullah; MouhoubThe emergence and growth of the Internet of Things (IoT) have changed how we live and interact with technology. The seamless integration of connected devices, from household to industrial equipment, has brought about a new era of interconnectedness. However, this rapid expansion of the IoT also introduces new security concerns that need to be assessed. Assessing the security risks associated with deploying and using this technology is crucial. Consequently, organizations need a risk assessment framework that helps identify, evaluate, and manage the risks of IoT, including data privacy and confidentiality, system integrity, availability, and performance. The state-of-the-art has been given significant attention to security risk assessment in traditional cybersecurity with powerful computer systems, but the challenges of deploying IoT devices and their associated vulnerabilities have been overlooked. In this thesis, we first present a novel IoT security risk assessment framework for the healthcare environment, in which we have improved upon existing methodologies. The proposed framework dynamically calculates the risk score for different device profiles, considering their population and other parameters, such as network protocols, device heterogeneity, device security updates, device physical security status, device history status, layer history status, and device criticality. Second, we present a customizable framework for assessing the security risk of deploying and utilizing IoT devices in various environments. We dynamically calculate risk scores for different devices, considering their importance to the system and their vulnerabilities, among other parameters. The customizable framework considers the important parameters of the devices, their vulnerabilities, and how they impact the overall risk assessment. The importance of these devices and the severity of vulnerabilities are incorporated in the framework using the well-known Multi-Attribute Decision Making (MADM) methods, namely, Simple Additive Weighting (SAW) and Weighting Product (WP). Finally, the risk is assessed on a setup comprised of IoT devices widely deployed in healthcare systems, such as emergency rooms.12 0Item Restricted Integrating Industry 4.0 in Project Management: A Systematic Literature Review(De MontFort University, 2024-09-20) almehaize, Ghannam nasser; Oyinlola, AdewaleThis thesis investigates Industry 4.0 technologies with the aim of integrating them into project management methodologies to improve efficiency, decision-making, and overall project success. The study investigates the existing studies on the influence of these technologies on project management processes and evaluates the present status of their integration across a variety of sectors. This is accomplished via a comprehensive examination of the available literature and studies. Industry 4.0 technologies have the potential to revolutionise project management by enabling the sharing and analysis of real-time data, according to the results. In addition, they present challenges regarding organisational culture, communication, and skill limitations. This thesis shows that project managers need technical understanding, leadership, and flexibility. This thesis ultimately emphasises the potential of Industry 4.0 technologies to enhance project performance, while also emphasising the need for organisations to modify their project management frameworks in order to prosper in a digital environment that is swiftly evolving. In order to enable organisations to fully realise the promise of these technologies for successful and sustainable development, the study's conclusion calls for further research to develop frameworks that facilitate the effective integration of these technologies.11 0Item Restricted Assessing and Enhancing Protection Measures for Internet of Things (IoT) in Cybersecurity(University of Portsmouth, 2024-09) Alshehri, Abdulrahman; Bader-El-den, MohammedThe Internet of Things (IoT) revolution sweeps across Saudi Arabia, connecting devices, transforming industries, enhancing lives. But with great connectivity comes great vulnerability - cybersecurity threats loom large in this digital frontier. This study delves into the heart of IoT security in the Kingdom, surveying the landscape, probing the defenses, seeking solutions. Through the lens of cybersecurity professionals, we explore current practices, uncover challenges, envision improvements. Our findings paint a picture of a nation at a crossroads: frequent audits needed, authentication protocols lacking, employee training insufficient, encryption underutilized. Yet hope springs eternal in the form of correlations discovered - more vigilant monitoring begets stronger authentication desires. From this research emerges a roadmap for the future: recommendations for policymakers to craft robust regulations, guidelines for organizations to fortify their digital fortresses, advice for end-users to navigate the IoT maze safely. In the rapidly evolving technological tapestry of Saudi Arabia, this study weaves a thread of security consciousness, contributing to a safer, more reliable IoT ecosystem. As the Kingdom marches towards its Vision 2030, may it do so with cybersecurity as its steadfast companion.13 0Item Restricted AN INTEGRATED DIGITAL TWIN FRAMEWORK AND EVACUATION SIMULATION SYSTEM FOR ENHANCED SAFETY IN SMART BUILDINGS(Western Michigan University, 2024-06-29) Almatared, Manea Mohammed S; Liu, HexuFire hazards in buildings continue to pose a substantial risk to human life and property safety despite declining deaths, injuries, and damages over the past decade. Consequently, fire safety management (FSM) is crucial to effectively preventing and controlling fire hazards. However, several challenges need to be addressed to ensure optimal FSM in buildings, such as the lack of effective integration of advanced technologies such as Internet of Things (IoT) sensors, fire detection systems, and automated response mechanisms, the reliance on insufficient fire safety equipment (FSE) maintenance and a lack of operational skills among occupants. In particular, traditional manual methods of searching for information, such as using two-dimensional drawings and relying on paper documents, have become inefficient and costly as buildings have become larger and more complex. This leaves room for improvement in current FSM practices— specifically, high-efficiency evacuation- the best approach for minimizing mortality and property loss. Digital twin (DT) technologies have been widely used in other industries, such as manufacturing and transportation, to improve efficiency, reduce costs, and enhance safety. However, the FSM sector has been a slow adopter of DT technology. This study investigated the adoption of DT technologies in the FSM sector. This research aims to explore the limitations, opportunities, and challenges associated with adopting DT technology in the FSM sector and further develop a DT-based FSM framework towards smart facility management (FM). This framework lets decision-makers obtain comprehensive information about the building's communication and safety systems. It can also enable the real time monitoring of FSE and provide predictive maintenance. Toward this objective, several DTs for FSM were first reviewed, including building information modeling (BIM), the Internet of Things (IoT), artificial intelligence (AI), and augmented reality (AR). These technologies can be used to enhance the efficiency and safety of FSM in smart buildings. The framework was then synthesized based on the literature review, application requirements, and industry needs. A questionnaire survey was conducted for FM professionals to evaluate the framework and identify the challenges of adopting DT and the proposed framework in the FSM sector. The survey results identify the current state of DT technology in the FSM sector, provide insights into the perception of DT technology among FM practitioners, and validate its expected benefits and potential challenges. The main barriers to adopting DTs in FSM are a lack of knowledge about DTs, their initial costs, user acceptance, difficulties in systems integration and data management, education training costs, a lack of competence, development complexity, and data security. Furthermore, the research develops a building fire evacuation simulation system based on the validated framework, i.e., smart lighting. This system integrates the data from the BIM platform, Fire Dynamic Simulator (FDS), and Agent-Based Simulation (ABS) platform for evacuation through customized developments. Real-time fire situation is transmitted to the evacuation simulation platform to assess the impact of dynamic fire spread on the evacuation of people. A model for optimizing evacuation route planning is designed to improve the utilization of each evacuation exit and provide a visualization of evacuation routes as smart lighting in Dynamo. This proposed system was validated by conducting a case study on three fire evacuation scenarios. An average of 20.9 % increases the evacuation efficiency in three scenarios. The main contributions of this research include (1) Developing a DT-based FSM framework for smart buildings, (2) Developing a fire emergency evacuation simulation system for buildings by integrating DT technologies, and 3) Achieving the integration and interoperability of BIM data, fire data, and evacuation data from different platforms.34 0Item Restricted Machine Learning (ML) Technologies(John Jay College of Criminal Justice, 2024-04-03) Alanazi, Mosa; Seferaj, GentianaIntegrating Machine Learning (ML) technologies into physical security has ignited significant discourse within scholarly circles, focusing on identifying specific ML technologies currently employed and elucidating their tangible outcomes. This integration occurs against a rapidly evolving technological landscape, encompassing advancements such as cloud computing, 5G wireless technology, real-time Internet of Things (IoT) data, surveillance cameras fortified with biometric technologies, and predictive data analytics. Collectively, these innovations augment the transformative potential of ML within security frameworks, ranging from sophisticated video analytics facilitating advanced threat detection to predictive algorithms aiding in comprehensive risk assessment. Moreover, the seamless fusion of disparate data streams and the capability to extract actionable insights in real-time present profound implications for the future trajectory of security protocols, heralding a paradigm shift in the conceptualization, implementation, and Student No: 10001 Page 2 of 14 Comprehensive Exam/Project ̶̶̶ Spring24 Department of Security, Fire and Emergency Management maintenance of physical security measures. This study endeavors to delve into the specifics of ML technologies currently operationalized in physical security contexts, scrutinize the tangible outcomes they yield, and forecast how these trends will shape the future security landscape— additionally, strategic recommendations aimed at optimizing the efficacy of ML-driven security solutions in safeguarding physical environments.133 0Item Restricted IOT-BASED INDOOR AIR QUALITY MONITORING SYSTEM FOR ENHANCED OCCUPANT HEALTH AND COMFORT IN SMART BUILDINGS(Dublin City Univercity, 2023-08-16) Altamimi, Mshal Baker; Intizar, Ali; Coyle, Shirley; Manzke, MingmingIoT-based indoor air quality (IAQ) monitoring systems hold significant importance in enhancing occupant health and comfort within the context of smart buildings. The quality of indoor air directly impacts the well-being, productivity, and overall quality of life of individuals residing or working within enclosed spaces. Poor IAQ, characterized by elevated pollutant levels, can lead to a range of adverse health effects, including respiratory ailments, allergies, and chronic illnesses. This paper focuses on the experience and implementation of an IoT-based IAQ monitoring platform with three sensors. The platform uses the IoT Adafruit database dashboard and Telegram server to observe indoor air quality in a selected place, at any time. Based on IoT technology, the sensors are created to monitor air quality effectively and send data to the dashboard and Telegram through Internet. The project is made up of Esp32 microprocessor, sensors for pollution, and temperature humidity detection. The systems used in this study are made to measure the voltage levels of MQ4, MQ-135, and DHT11 to track the air quality and gas. After that, the project was tested successfully, where all sensors get the correct percentages.4 0