Saudi Cultural Missions Theses & Dissertations
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Item Restricted Cyber Physical Attacks and Detection MEthods in Water Distribution Systems(The University of Alabama, 2025) Hameed Addeen, Hajar; Yang, XaioModern technologies adopt Internet of Things (IoT) devices to increase water management efficiency and enhance water quality services. However, the limitations of IoT devices, such as small sizes and poor security, weaken the Water Distribution System (WDS) security and many attackers compromise the critical components of WDS. Cyber-physical attacks (CPAs) are considered one of the biggest challenges that decrease the security factors in WDS by disrupting normal operations and tampering with the critical data of the water system. Therefore, this dissertation proposes an anomaly detection method to detect cyber-physical attacks and mitigate their bad impacts on the components of WDS. First, we study the current state-of-art for the common cyber-physical attacks and common detection mechanisms for the WDS. Also, we compare CPA attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, and limitations. Second, we propose a deep learning model based on a conditional variational autoencoder (CVAE) to detect cyber-physical attacks. The CVAE model shows a highly effective way to maximize the chance of generating the desired output and detecting CPA attacks quickly. We also train CVAE on (BATADAL) real medium-sized water distribution dataset and demonstrate high-efficiency results. Experiment results indicate that our proposed method of CVAE can detect all the listed attacks with high accuracy and reduce false alarm issues. Then, we evaluate the proposed models’ performance using various metrics, including accuracy, precision, recall, and F1 score. In addition, we compare the CVAE model with existing models to detect CPA attacks, and the results show that we reach a high accuracy of 98%. Third, we designed an adversarial attack on our CVAE model to show the security risks of this attack and the negative impact on the model’s accuracy. We apply the Fast Sign gradient method to fool the CVAE model and predict the anomalies as normal data rather than anomalies. Then, we propose our novel defense approach, the CVAE defense model, to detect adversarial attacks. The CVAE defense model consists of adversarial detection and the CVAE defense models. The adversarial detection model adopts CNN and LSTM methods to classify data as adversarial or clean. The CVAE defense model takes the output of the adversarial detection model and passes it to our proposed noise generation method. After that, the noise generation method is produced and passed to the CVAE model and activation function. Finally, we calculate the Euclidean distance between the reconstructed output and input vectors and compare it to the threshold. If the output is less than the threshold, there is no attack. Otherwise, the output should be one, and there is an attack. The results show that our CVAE defense model can detect adversarial attacks and increase the performance to an overall 92%.15 0Item Restricted Investigating IoT Adoption in the Face of Organizational Culture and Uncertainty Challenges: The Mediating Role of Information Behaviors on Communication Quality(Saudi Digital Library, 2025-05-17) ALASMARI, AHMAD; Shabtai, ItamarAbstract Investigating IoT Adoption in the Face of Organizational Culture and Uncertainty Challenges: The Mediating Role of Information Behaviors on Communication Quality By Ahmad Ali Alasmari Claremont Graduate University: 2025 The successful integration of Internet of Things (IoT) technologies in higher education institutions necessitates agile service deployment, transparent communication, and cultural congruence. Guided by Social Cognitive Theory (SCT) and Diffusion of Innovation (DOI) frameworks, this mixed-methods study examines the interconnections between High Uncertainty Avoidance, Organizational Culture, Information Availability, Information-Seeking Behavior, and IoT Service Agility and their collective impact on Communication Quality at the University of Tabuk in Saudi Arabia. Employing a sequential explanatory design, quantitative data from 373 survey respondents was augmented by qualitative insights from seven in-depth interviews with faculty, administrative staff, and IT management. Findings demonstrate that high uncertainty avoidance significantly diminishes IoT agility and adversely affects communication quality, while a supportive organizational culture enhances communication effectiveness. The relationships among cultural characteristics, IoT agility, and communication quality are mediated by proactive information behaviors, specifically information seeking and availability. The study provides practical strategies to overcome cultural and uncertainty-related barriers, including enhancing information transparency, implementing structured training programs, and fostering proactive leadership engagement. These insights offer valuable guidance for Saudi Arabian higher education institutions and similar contexts aiming to optimize IoT technology adoption.2 0Item Restricted Hospital's Agility and Lean in International Supply Chain Management during COVID-19(University of Exeter, 2024) Albadawi, Abdulrahman Ismail; Sam, Abraham JohnThis hypothesis stems from the assumption that even though KFSHRC had efficient strategies in place, the highly unforeseen nature of the pandemic illustrated the strategies that still needed to be improved. To do this, the study seeks to reject the following hypothesis: this research will analyze the employees' experiences and compare the efficiency of the approaches used in the hospital. The research aims to fill these gaps and limitations to evaluate KFSHRC precisely during the crisis response and recommend future improvements.6 0Item Restricted IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities(Newcastle University, 2025-05) Almutairi, Reham; Graham, Morgan; Giacomo, BergamiThe Internet of Things (IoT) has rapidly evolved over the past two decades, transforming the way we interact with the environment through a network of interconnected devices. The purpose of this thesis is to explore the integration of IoT with Vehicular Ad-Hoc Net- works (VANETs) in order to enhance intelligent transportation systems (ITS) and smart city infrastructure through the use of IoT. VANETs, characterized by high mobility and dynamic topology, play a crucial role in enhancing traffic safety, efficiency, and vehicu- lar services. They improve traffic safety by enabling real-time communication between vehicles and roadside infrastructure, allowing the sharing of critical information such as accident warnings and road conditions to prevent collisions and enhance emergency re- sponse times. VANETs boost traffic efficiency through intelligent traffic management, optimizing signal timings and route planning based on real-time data to reduce con- gestion and travel times. Additionally, they provide enhanced vehicular services such as infotainment, navigation assistance, and maintenance alerts, thereby improving the overall driving experience and vehicle performance monitoring. This research addresses the significant challenges of simulating VANET environments, particularly the high mobility of vehicles and the need for realistic traffic scenarios. Ex- isting VANET simulators, while advanced, often lack support for new technologies and comprehensive security systems, highlighting the necessity for more comprehensive sim- ulation frameworks. The primary aim of this PhD thesis is to integrate IoT and traffic simulations to accurately evaluate vehicular energy efficiency and overall network perfor- mance. Therefore, this thesis presents multilateral research towards optimization, mod- eling, and simulation of VANET and IoT environments. Several tools and algorithms have been proposed, implemented, and evaluated, considering various environments and applications.15 0Item Restricted Deciphering Hand Movement Patterns During Driving Using Smartwatch Signals Without Ground Truth(University of Houston, 2025-02-07) Alghamdi, Huda; Pavlidis, IoannisWe developed a method to identify atypical hand movements in driving, some of which are associated with detachment from the steering wheel and, thus, physical distraction. We performed our data analysis on NUBI { a naturalistic dataset collected from a week-long observation of n=57 Texas drivers. NUBI features data from over 900 trips with a total duration of over 300 hours. Due to a lack of visual ground truth, we employed unsupervised learning methods. Thanks to the GPS data to our avail, we used information about the type of road (highway or city street) and the type of segment (straight or turn) to narrow our search space. In more detail, we extracted features from the drivers' smartwatch motion signals using Temporal Convolutional Autoencoder (TCNAE). Then, we fed these encoded features into a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN produced a main cluster and the remainder. The remainder is consistently associated with behaviors in turns and other atypical scenarios, such as queuing to pick up orders from fast-food dispensing windows. The characteristics of these atypical patterns are so distinct from the typical driving patterns (main cluster) that a random forest classi cation algorithm attained 99% area under the curve (AUC) performance in a ve-fold cross-validation test. Based on the kinematic constraints of the driver's hands, we developed a physics-based formula that associates elbow angles with gravitational acceleration values. We estimated the gravitational acceleration values that correspond to hand detachment from the steering wheel (i.e., extreme elbow angles). Applying these thresholds to the NUBI dataset, we found that such steering wheel detachment values arise just outside the dispensing windows of fast-food chains, where the drivers must pick up their orders. This nding validates our estimation method. In all, our approach not only nds atypical hand-motion patterns in driving but also pinpoints among these atypical patterns the patterns that involve hand detachment from the steering wheel. The latter are associated with physical distractions and crash risk under certain conditions. Notably, our approach achieves all these from smartwatch signals alone without any need to resort to visual ground truth from camera feeds. Given the ubiquity of smartwatches and the unavailability of cameras in car interiors, the practical implications of this development cannot be overestimated.25 0Item Restricted Decoding real world LR-FHSS signals: design, implementation, and approaching the theoretical limit.(Florida State University, 2025-03-26) Bukhari, Jumana; Zhang, ZhenghaoLong Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a new physical layer option added to the LoRa family, promising higher network capacity than the previous versions of LoRa. Since the announcement, LR-FHSS has gathered growing interest. Various studies have attempted to evaluate and enhance its communication range and network capacity, while others have compared its performance with previous version of LoRa. However, the actual network capacity of LR-FHSS and the effectiveness of proposed methods remain unknown, as most existing studies rely on mathematical analysis or simulations with certain simplifying assumptions. Our goal is to reveal the actual capacity of LR-FHSS and develop methods to enhance its performance while evaluating these methods in a setting as close to real-world conditions as possible. In this dissertation, we design and implement a software LR-FHSS receiver from scratch to convert the baseband waveform into bits and pass the Cyclic Redundancy Check (CRC). To the best of our knowledge, this work is the first of its kind that processes signals transmitted by an actual LR-FHSS device while accounting for real-world issues such as frequency estimation errors. Also, we design customized methods to enhance receiver's capacity, including error correction decoding and Successive Interference Cancellation (SIC), which were not mentioned in earlier studies but effectively handle collisions. Furthermore, we develop an analytical bound for the theoretical capacity of LR-FHSS networks. The evaluation of our receiver was based on real-world packet traces collected using an LR-FHSS device and demonstrated through real-world experiments on the POWDER wireless platform, in addition to trace-driven simulations for large networks. Our result shows that LR-FHSS outperforms the previous version of LoRa, meets expectations in communication range, achieves significantly higher network capacity than those reported earlier, and confirms that the capacity of our software receiver is approaching the upper bound of LR-FHSS networks. We overcame a number of challenges such as lack of documentation of LR-FHSS and open-source resources, header acquisition, match reconstructed waveform with the received waveform under heavy collisions, and find a good approximation of the residual power of packets that have been decoded and canceled.7 0Item Restricted The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, ThomasThis research explores the implementation of IoT-based predictive maintenance within railway systems, focusing on the technologies, cost implications, reliability, safety, and barriers identified in the literature. The study systematically reviews 30 peer-reviewed journals to assess the current state of IoT applications in the railway sector. Critical IoT technologies such as sensors, wireless sensor systems, and edge processing are examined in their role in enhancing predictive maintenance practices. The research highlights significant long-term cost savings associated with IoT adoption, despite high initial implementation costs. Furthermore, the study evaluates how IoT technologies contribute to improved reliability and safety by enabling real-time monitoring and predictive analysis. However, several barriers to widespread adoption are identified, including technical integration challenges, financial constraints, regulatory hurdles, and organisational resistance. The findings underscore the need for a strategic approach that will help tackle all obstacles by realising the benefits of IoT-predictive maintenance in the railway sector. This study offers significant insights for stakeholders, offering a deep understanding of the challenges of IoT-based predictive maintenance in railways. Future research directions are suggested, emphasising the importance of long-term studies, holistic approaches, and the integration of emerging technologies to address the identified barriers.28 0Item Restricted Simplifying IoT Application Deployment in Edge Computing Environment(newcastle university, 2019) Daghistan, iIsmail; Ranjan, RajivCurrently, deploying IoT Application in Edge Computing Environment is facing many challenges. For instance, the limited capabilities of Edge device alongside distributed nature of its Environment where all Edge devices needs to be programmed separately. As a result, edge Environment deployment tools needs to be lightweight and able to orchestrate the deployment effectively. Therefore. I decided to deploy Edge Computing Environment by using docker. Docker proved to be the most suitable tool. This is because Docker is lightweight and ability to run on the Edge . Then, I decided to simplify the deployment by using scripting, to allow the deployment to take place in on device rather than programming several devices. Finally, I will use REST api to capture the user’s requirements to generate and run a script based the that requirements. This will the user to deploy the Edge Environment with a single URL request.10 0Item Restricted A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, EllisRecently, it has been appealing to integrate Blockchain with IoT in several domains, such as healthcare and smart cities. This integration facilitates the decentralized processing of IoT data, enhancing cybersecurity by ensuring data integrity, preventing tampering, and strengthening privacy through decentralized trust mechanisms and resilient security measures. These features create a secure and reliable environment, mitigating potential cyber threats while ensuring non-repudiation and higher availability. However, Blockchain performance is questionable when handling massive data sets generated by complex and heterogeneous IoT applications. Thus, whether the Blockchain performance meets expectations will significantly influence the overall viability of integration. Therefore, it is crucial to evaluate the feasibility of integrating IoT and Blockchain and examine the technology readiness level before the production stage. This thesis addresses this matter by extensively investigating approaches to the performance evaluation of Blockchain-based IoT solutions. Firstly, it systematically reviews existing Blockchain simulators and identifies their strengths and limitations. Secondly, due to the lack of existing blockchain simulators specifically tailored for IoT, this thesis contributes a novel blockchain-based IoT simulator which enables investigation of blockchain performance based on adaptable design configuration choices of IoT infrastructure. The simulator benefits from lessons learnt about the strengths and limitations of existing works and considers various design requirements and views collected through questioners and focus groups of domain experts. Third, the thesis recognises the shortcomings of blockchain simulators, such as support for smart contracts. Therefore, it contributes a middleware that leverages IoT simulators to benchmark real blockchain platforms' performance, namely Hyperledger Fabric. It resolves challenges related to integrating distinctive environments: simulated IoT models with real Blockchain ecosystems. Lastly, this thesis employs Machine Learning (ML) techniques for predicting blockchain performance based on predetermined configurations. Contrariwise, it also utilises ML techniques to recommend the optimal configurations for achieving the desired level of blockchain performance.70 0Item Restricted INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, AvielThe proliferation of interconnected smart devices, once ordinary household appliances, has led to an exponential increase in sensitive data collection and transmission. The security and privacy of IoT devices, however, have lagged behind their rapid deployment, creating vulnerabilities that can be exploited by malicious actors. While security attacks on IoT devices have garnered attention, privacy implications often go unnoticed, exposing users to potential risks without their awareness. Our research contributes to a deeper understanding of user privacy concerns and implications caused by data collection within the vast landscape of the Internet of Things (IoT). We uncover the true extent of data accessible to adversarial individuals and propose a solution to ensure data privacy in precarious situations. We provide valuable insights, paving the way for a more informed and comprehensive approach to studying, addressing, and raising awareness about privacy issues within the evolving landscape of smart home environments.15 0
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