Saudi Cultural Missions Theses & Dissertations

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Cyber Physical Attacks and Detection MEthods in Water Distribution Systems
(The University of Alabama, 2025) Hameed Addeen, Hajar; Yang, Xaio
Modern 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%.
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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, Itamar
Abstract 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.
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Hospital's Agility and Lean in International Supply Chain Management during COVID-19
(University of Exeter, 2024) Albadawi, Abdulrahman Ismail; Sam, Abraham John
This 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.
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IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities
(Newcastle University, 2025-05) Almutairi, Reham; Graham, Morgan; Giacomo, Bergami
The 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.
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Deciphering Hand Movement Patterns During Driving Using Smartwatch Signals Without Ground Truth
(University of Houston, 2025-02-07) Alghamdi, Huda; Pavlidis, Ioannis
We 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.
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Decoding real world LR-FHSS signals: design, implementation, and approaching the theoretical limit.
(Florida State University, 2025-03-26) Bukhari, Jumana; Zhang, Zhenghao
Long 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.
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The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review
(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, Thomas
This 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.
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A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems
(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, Ellis
Recently, 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.
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INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES
(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, Aviel
The 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.
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Testing Privacy and Security of Voice Interface Applications in the IoT Era
(Temple University, 2024-04-04) Shafei, Hassan Ali; Tan, Chiu C.
Voice User Interfaces (VUI) are rapidly gaining popularity, revolutionizing user interaction with technology through the widespread adoption in devices such as desktop computers, smartphones, and smart home assistants, thanks to significant advancements in voice recognition and processing technologies. Over a hundred million users now utilize these devices daily, and smart home assistants have been sold in massive numbers, owing to their ease and convenience in controlling a diverse range of smart devices within the home IoT environment through the power of voice, such as controlling lights, heating systems, and setting timers and alarms. VUI enables users to interact with IoT technology and issue a wide range of commands across various services using their voice, bypassing traditional input methods like keyboards or touchscreens. With ease, users can inquire in natural language about the weather, stock market, and online shopping and access various other types of general information. However, as VUI becomes more integrated into our daily lives, it brings to the forefront issues related to security, privacy, and usability. Concerns such as the unauthorized collection of user data, the potential for recording private conversations, and challenges in accurately recognizing and executing commands across diverse accents, leading to misinterpretations and unintended actions, underscore the need for more robust methods to test and evaluate VUI services. In this dissertation, we delve into voice interface testing, evaluation for privacy and security associated with VUI applications, assessment of the proficiency of VUI in handling diverse accents, and investigation into access control in multi-user environments. We first study the privacy violations of the VUI ecosystem. We introduced the definition of the VUI ecosystem, where users must connect the voice apps to corresponding services and mobile apps to function properly. The ecosystem can also involve multiple voice apps developed by the same third-party developers. We explore the prevalence of voice apps with corresponding services in the VUI ecosystem, assessing the landscape of privacy compliance among Alexa voice apps and their companion services. We developed a testing framework for this ecosystem. We present the first study conducted on the Alexa ecosystem, specifically focusing on voice apps with account linking. Our designed framework analyzes both the privacy policies of these voice apps and their companion services or the privacy policies of multiple voice apps published by the same developers. Using machine learning techniques, the framework automatically extracts data types related to data collection and sharing from these privacy policies, allowing for a comprehensive comparison. Next, researchers studied the voice apps' behavior to conduct privacy violation assessments. An interaction approach with voice apps is needed to extract the behavior where pre-defined utterances are input into the simulator to simulate user interaction. The set of pre-defined utterances is extracted from the skill's web page on the skill store. However, the accuracy of the testing analysis depends on the quality of the extracted utterances. An utterance or interaction that was not captured by the extraction process will not be detected, leading to inaccurate privacy assessment. Therefore, we revisited the utterance extraction techniques used by prior works to study the skill's behavior for privacy violations. We focused on analyzing the effectiveness and limitations of existing utterance extraction techniques. We proposed a new technique that improved prior work extraction techniques by utilizing the union of these techniques and human interaction. Our proposed technique makes use of a small set of human interactions to record all missing utterances, then expands that to test a more extensive set of voice apps. We also conducted testing on VUI with various accents to study by designing a testing framework that can evaluate VUI on different accents to assess how well VUI implemented in smart speakers caters to a diverse population. Recruiting individuals with different accents and instructing them to interact with the smart speaker while adhering to specific scripts is difficult. Thus, we proposed a framework known as AudioAcc, which facilitates evaluating VUI performance across diverse accents using YouTube videos. Our framework uses a filtering algorithm to ensure that the extracted spoken words used in constructing these composite commands closely resemble natural speech patterns. Our framework is scalable; we conducted an extensive examination of the VUI performance across a wide range of accents, encompassing both professional and amateur speakers. Additionally, we introduced a new metric called Consistency of Results (COR) to complement the standard Word Error Rate (WER) metric employed for assessing ASR systems. This metric enables developers to investigate and rewrite skill code based on the consistency of results, enhancing overall WER performance. Moreover, we looked into a special case related to the access control of VUI in multi-user environments. We proposed a framework for automated testing to explore the access control weaknesses to determine whether the accessible data is of consequence. We used the framework to assess the effectiveness of voice access control mechanisms within multi-user environments. Thus, we show that the convenience of using voice systems poses privacy risks as the user's sensitive data becomes accessible. We identify two significant flaws within the access control mechanisms proposed by the voice system, which can exploit the user's private data. These findings underscore the need for enhanced privacy safeguards and improved access control systems within online shopping. We also offer recommendations to mitigate risks associated with unauthorized access, shedding light on securing the user's private data within the voice systems.
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