Theses for Computer Science

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Enhancing Arabic Offensive Tweet Classification: An Ensemble Approach Integrating AraBERT, Neural Networks, and LSTM Models
(The British University in Dubai (BUiD), 2023-10) WAHDAN, AHLAM MOHAMMAD; Professor Khaled Shaalan; Dr Mostafa AL-Emran
This thesis addresses the crucial research problem of accurate detection and moderation of offensive language in Arabic text, considering the intricacies posed by the language's complex morphology, dialectal variations, orthographic ambiguity, orthographic noise, limited linguistic resources, and the necessity for comprehensive coverage of offensive language expressions. The research objectives are delineated through four key research questions. Firstly, the study aims to identify the existing research gaps in Arabic Text Classification (ATC) through an extensive and rigorous systematic literature review. The study adopts a scholarly and formal approach, aiming to identify the specific areas within ATC research that lack comprehensive exploration or exhibit inadequacies in existing knowledge. This endeavor is grounded in the rigorous analysis and synthesis of relevant academic literature, ensuring a meticulous examination of the current state of research in ATC. Secondly, it investigates the effects of employing novel pre-processing methods on the performance of Arabic Text Classification. Thirdly, the research endeavors to determine the most effective model for enhancing the accuracy of Arabic offensive text classification by introducing a novel approach using pre-trained models; AraBERT model in conjunction with fully connected neural networks (NN) and long short-term memory (LSTM) networks. Finally, the study evaluates the proposed model's ability to classify Arabic offensive text effectively. The research methodology consists of two integral parts, comprising dataset description, the proposed framework. The dataset description provides insights into the two datasets utilized, namely OSACT and SEMEval. The framework elucidates the proposed model, which leverages a combination of pretrained models and neural networks, thereby achieving a high level of effectiveness in classifying Arabic offensive text. The model's performance is meticulously assessed using various evaluation metrics, including accuracy and F1-macro score, and is compared against other classifier models. The research findings demonstrate the superiority of the proposed model over the baseline AraBERT model, with the proposed model achieving an accuracy of 0.870 compared to the baseline accuracy of 0.820, along with an F1-score of 0.853 compared to the baseline's 0.800. This emphasizes the model's exceptional capacity to accurately identify offensive content in Arabic text. The implications of this research extend to diverse domains and stakeholders, encompassing decision makers, developers, and policy makers. The insights garnered from the study can be instrumental in making informed decisions pertaining to the integration of Arabic text classification systems in various operational settings. By comprehending the proposed model's performance and efficacy, decision makers can assess its potential impact on optimizing processes such as information retrieval, content filtering, and sentiment analysis in Arabic text. In conclusion, this thesis contributes significantly to the existing literature by addressing the complexities associated with offensive language identification in Arabic text and introducing an innovative approach that integrates pretrained models with deep learning techniques and neural networks. The demonstrated effectiveness and superior performance of the proposed model underscore its potential for practical implementation in real-world scenarios, thereby bolstering the field of Arabic offensive text classification.
Sentiment Analysis in Social Media: A Deep Learning Framework for Analyzing Public Sentiment toward Policies during Natural Disasters
(The British University in Dubai (BUiD), 2023-07) FOOLAD, MOHAMED ABDULLA; Professor Sherief Abdallah
The primary aim of this study is to produce and present a robust, reliable, and accurate framework to measure and analyze public sentiments for decision-makers and policies legislators while managing and administrating the spread of a pandemic or a natural disaster in general. The study utilizes COVID-19 as an actual case study to evaluate the proposed framework, and tests relevant hypotheses. The study used word embedding as a feature vector and then tested the accuracy of different models such as LSTM, Transformers, Logistic regression, Random Forest Classifier, KNN, and Multinomial Naïve Bayes. This work used a deep learning model –LSTM model on the collected tweets to classify the emotions and reactions. The models applied in the classification of COVID-19 global data performed differently with regard to accuracy and precision. Transformers emerged as the most accurate model, with a precision score of (90.18%) and an accuracy of (90.17%). LSTM was also superior with regard to the accuracy, with an (88.45%) precision score and an accuracy of (88.44%). The Random Forest classifier performed fairly, with a (52.43%) precision score and (51.26%). The other models performed poorly in predicting sentiments; for instance, Multinomial Naïve Bayes recorded (43.54%) precision and (41.23%) prediction accuracy, the logistic regression model recorded a (43.54%) precision and (41.23%) prediction accuracy, and the KNN model had (41.23%) precision and (41.11%) prediction accuracy. Overall, LSTM was established as the most suitable model for the selected dataset since it was able to fit the dataset and can be generalized to the current and new datasets, unlike transformers.
Exploring the Factors Affecting Chatbot Use in Higher Education and Its Impact on Social Sustainability Using a Hybrid SEM-ANN Approach
(The British University in Dubai (BUiD), 2023-06) ALNUAIMI, ABDULLA ALSHARHAN; Professor Khaled Shaalan
Previous studies have identified various drivers of chatbot adoption through different technology adoption theories. However, these studies have not been thoroughly reviewed and synthesized. Most studies have focused on examining the intention of using chatbots, with limited investigations on actual use and sustainable intention. This thesis explores the factors influencing the sustainable adoption of chatbots in higher education, specifically focusing on social sustainability. It aims to develop an integrated model that comprehensively examines the influencing factors, moderating effects, and organizational dynamics that shape chatbot adoption. The research begins with a comprehensive literature review to identify factors that generally influence chatbot adoption. These factors encompass technological, individual, organizational, and contextual dimensions. Building upon this literature review, the conceptual model is developed, addressing the research gaps in the existing literature and focusing on chatbot acceptance in the higher education sector. The systematic review includes an analysis of empirical studies published between 2016 and September 2022, resulting in 219 eligible studies out of 3,942 reviewed. The main findings reveal that the Technology Acceptance Model (TAM), Social Presence Theory (SPT), and Computers as Social Actors (CASA) are the prevailing theories explaining chatbot adoption. Anthropomorphism is the most examined external factor, followed by trust, enjoyment, and interactivity. The conceptual framework integrates established theories such as Task-Technology Fit (TTF), Source Credibility Theory, Social Presence Theory (SPT), and additional factors specific to chatbot adoption in higher education. A quantitative survey is administered to a sample of 341 individuals and students from the higher education sector in the UAE, capturing diverse perspectives on chatbot adoption. The model is then validated using advanced analytical techniques, including Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Networks (ANN). The findings contribute to understanding chatbot adoption in higher education, providing insights into key drivers, challenges, and implications for social sustainability. The study's findings highlight factors such as task-technology fit, credibility, and social presence that significantly influence the intention to sustainably use chatbots in higher education. The sensitivity analysis reveals the importance of social presence, followed by credibility and task-technology fit, in influencing chatbot use. The chatbots’ technological characteristics have a greater impact than the task characteristics, and visual cues are perceived as more important than invisible and verbal cues for chatbot social presence. Trustworthiness is the most significant factor impacting credibility, followed by ease of use, tailoring, and commercial implications. However, Expertise, Real-World Feel, and Amateurism do not significantly impact credibility. These results contribute to developing acceptance models that can guide the design, implementation, and evaluation of chatbot initiatives in higher education institutions, fostering social sustainability. The theoretical contributions lie in developing an integrated model that extends existing theories to the context of chatbot adoption in higher education. The model provides a comprehensive understanding of the influencing factors and their interrelationships, offering a valuable framework for future research. From a practical perspective, the findings assist higher education institutions in strategically implementing and managing chatbots. The insights gained from this study can guide the development of effective strategies to promote chatbot acceptance, address privacy concerns, and leverage chatbots' potential for enhancing social sustainability in higher education settings.
Detection of Depression in Arabic Social Media: A Comparison of Traditional and Modern Machine Learning Algorithms
(The British University in Dubai (BUiD), 2023-12) ALSHEHHI, OMAR KHALID HAMAD; Professor Sherief Abdallah
This study aims to address the research gap in detecting depression from Arabic tweets using the PHQ-9 scale as a framework. The dataset collected was a set of 200,000 tweets from around 20,000 users. A team of psychologists and assistants used a user-based approach to label users as either depressed or not. The data labelling and annotation process involved a user-based evaluation of the tweets to label users as either depressed or not, based on the two target variables of depressed_binary and depressed_multi. Users with scores between 0 and 6 were categorized as not depressed in the depressed_binary variable, while those with scores above six were classified as depressed. For the depressed_multi variable, users with scores ranging from 0 to 2 were labelled as not depressed, scores from 3 to 6 indicated mild depression, scores from 7 to 9 indicated moderate depression and scores of 10 or above represented high depression. Four machine learning models were employed in this study: HGB (Histogram Gradient Boost), GRU (Gated Recurrent Units), LSTM (Long Short-Term Memory), and SVM (Support Vector Machines). The findings revealed that the older models exhibited strong performance in binary classification, while the new models demonstrated competitive results. Future research should focus on exploring and developing newer deep learning models, such as HGB and GRU models, to enhance the accuracy and performance of depression detection in Arabic tweets. Future studies should also investigate strategies to account for the influence of different Arabic dialects and incorporate Arabic colloquialisms in depression detection models.
Sentiment Analysis of the Emirati Dialect text using Ensemble Stacking Deep Learning Models
(The British University in Dubai (BUiD), 2023-03) AL SHAMSI, ARWA AHMED; Professor Sherief Abdallah
The study of thoughts, feelings, judgments, values, attitudes, and emotions regarding goods, services, organizations, persons, tasks, occasions, titles, and their attributes is known as sentiment analysis and it involves a polarity classification task for recognizing positive, negative, or neutral text to quantify what individuals believe using textual qualitative data. The rise of social media platforms provided an excellent source for sentiment analysis data. People use these platforms for various reasons, ranging from sharing their opinions and thoughts to gaining knowledge. Twitter, Instagram, and Facebook are examples of social media platforms. As more users join social media platforms, the amount of data that is generated online continues to grow at an accelerating pace. Most of the previous research that studied sentiment analysis for the Arabic language focused on Modern Standard Arabic and Egyptian, Saudi, Algerian, Jordanian, Tunisian, and Levantine Dialects. However, to our knowledge, no study involved employing deep learning models to conduct sentiment analysis on the Emirati dialect texts. Dialects are the informal form of a language. Each country of the Arab world has its own Dialect, and each dialect may have several sub-dialects. The main objective of this study is to develop a deep learning model that outperforms the state-of-the-art for Sentiment Analysis of the Emirati Dialect. Toward this objective, I first conducted a systematic review to identify the research gaps in the existing literature and investigate the available constructed resources for Arabic dialects and the used approaches for sentiment analysis of Arabic dialects. The systematic review focused on empirical research on the subject of Sentiment Analysis of the Arabic Dialect that was released between January 2015 and January 2021. Through the analysis, I found, with the exception of a few articles that investigated Saudi, Levantine, Jordanian, Algerian, Tunisian, and Egyptian dialects, researchers rarely specified the dialect type in their papers; instead, it was mostly mentioned (MSA and Arabic Dialects). Emirati Dialect has not been explored for Sentiment Analysis purposes. The sizes of most datasets of previous research were between 10,000 and 50,000. Moreover, the Twitter platform was the most popular online platform for constructing Arabic datasets. Most of the studies evaluated basic Machine Learning approaches for Sentiment Analysis of Arabic Dialects. My research aims to fill these identified gaps as I detail below. Since Instagram is one of the most popular social media platforms in UAE, I constructed a dataset of the Emirati dialect from the Instagram platform. My dataset consists of 216,000 posts, of which 70,000 posts were manually annotated by three human annotators. Each post is annotated into (Positive/ Negative/Neutral), and it is further annotated into (Emirati Dialect/ Arabic Dialect/ MSA). In order for the dataset to be used as a benchmark, the inter-annotator agreement (IAA) was measured using Fleiss's Kappa coefficient. The findings reveal that the overall Fleiss Kappa coefficient is = 0.93, indicating an almost-perfect agreement amongst the three annotators. Once the dataset was constructed and validated, I then conducted a performance evaluation and comparison of various basic Machine Learning algorithms, Deep Learning models, and stacking deep learning models on different datasets of Sentiment Analysis of Arabic Dialects. For the basic machine learning algorithms, LR, NB, SVM, RF, DT, MLP, AdaBoost, GBoost, and an ensemble model of machine learning classifiers were used. For deep-learning models, CNN, Bi-LSTM, Bi-GRU, as well as Hybrid deep-learning models were used for Sentiment Analysis. In order to improve performance further, I have proposed three ensemble-stacking deep-learning models with meta-learner layers of classifiers. The first stacking deep learning model combined 2 of the used deep learning models that produced the best results in terms of accuracy, the second stacking deep learning model combined 4 of the used deep learning models that produced the best results in terms of accuracy, and the final stacking deep-learning model combined all the trained deep learning models in this research. The proposed ensemble stacking model was evaluated using three datasets: the ESAAD Emirati Sentiment Analysis Annotated Dataset (which is one of this thesis contributions), and two other benchmark datasets (A Twitter-based Benchmark Arabic Sentiment Analysis Dataset ASAD and Arabic Company Reviews dataset). Experimental results show that my proposed ensemble stacking model outperformed existing deep learning models and achieved an accuracy of 95.54% for the ESAAD dataset, 96.71% for the benchmark ASAD, and 96.65% for the Arabic Company Reviews Dataset.
Cyberbullying Detection in Arabic Text using Deep Learning
(The British University in Dubai (BUiD), 2023-03) ALBAYARI, REEM RAMADAN SA’ID; Professor Sherief Abdallah
In the new era of digital communications, cyberbullying is a significant concern for society. Cyberbullying involves the use of communication technology and data, including messages, photographs, and videos, to undertake aggressive negative actions to harm others. This practice has spread substantially due to rapid technological development and has gained significant attention in several domains involving data exchange, such as e-commerce, digital marketing, social media platforms, and others. Cyberbullying can negatively impact stakeholders, and can vary from psychological to pathological, such as self-isolation, depression, and anxiety potentially leading to suicide. Hence, detecting any act of cyberbullying in an automated manner will be helpful for stakeholders to prevent any unfortunate results from the victim’s perspective. If conducted automatically, rather than relying on human moderators, the process will be faster, enabling the early detection of cyberbullying before severe harm is caused. Data-driven approaches, such as machine learning (ML), particularly deep learning (DL), have shown promising results. DL approaches provide highly accurate predictive models for detecting cyberbullying. The first contribution of this thesis is conducting an in-depth meta-analysis of existing evaluation methods, classification techniques, and datasets related to ML for cyberbullying problems. The meta-analysis shows that ML approaches, particularly DL, have not been extensively studied for the Arabic text classification of cyberbullying. A potential reason for this research gap is the lack of Arabic-language repositories focusing on cyberbullying despite the large amount of Arabic text that can be extracted from Arabic social media platforms besides e-commerce and mobile applications. Consequently, I have designed and built a new Arabic text repository, the largest available, that can serve me and others in investigating various classifiers to deal with the issue of detecting cyberbullying. This repository contains 200,000 comments, 46,898 of which were annotated by three human annotators. First, the comments were classified as (positive/negative/neutral), and then the negative comments were further classified into two categories based on their level of negativity (toxic, bullying). The dialect for each comment was also added. This gives the dataset an advantage since it can be used for other purposes such as sentiment analysis and dialect identification, not just for cyberbullying detection. For the dataset to be regarded as a benchmark, Fleiss’s Kappa metric was adopted to measure the inter-annotator agreement (IAA), and the results show that the total Fleiss Kappa coefficient is = 0.869 with a p-value of 10-3, indicating near-perfect agreement among the three annotators. The application of DL to cyberbullying detection problems within Arabic text classification can be considered a novel approach due to the complexity of the problem and the tedious process involved, besides the scarcity of relevant research studies. Therefore, this study aims to evaluate several versions of Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FNNs) for detecting cyberbullying in the Arabic language. Although these algorithms are widely used in text classification and outperform the performance of classical classifiers, many have been extensively investigated in other domains such as sentiment analysis and dialect identification, as well as cyberbullying detection in English text. Hence, a comprehensive study focusing on Arabic cyberbullying can fill this gap in research. In this study, I conduct a performance evaluation and comparison for various DL algorithms (LSTM, GRU, LSTM-ATT, CNN-BLSTM, CNN-LSTM, CNN-BILSTM-LSTM, and LSTM-TCN) on different datasets of Arabic cyberbullying to obtain more precise and dependable findings. As a result of the models’ evaluation, a hybrid DL model is proposed that combines the best characteristics of the baseline models CNN, BLSTM and GRU for identifying cyberbullying. The proposed hybrid model improves the accuracy of all the studied datasets and can be integrated into different social media sites to automatically detect cyberbullying from Arabic social media posts. It has the potential to significantly reduce cyberbullying. Other results, related implications, and limitations, along with future research are also clarified and discussed.
Governmental Data Analytics: An Agile Framework Development and Real-World Data Analytics Case Studies
(The British University in Dubai (BUiD), 2023-03) QADADEH, WAFA; Professor Sherief Abdallah
A Decision Modelling Approach for Security Modules of Delegation Methods in Mobile Cloud Computing using Probabilistic Interval Neutrosophic Hesitant Fuzzy Set
(The British University in Dubai (BUiD), 2023-03) AL HANTOOBI, SENDEYAH
Mobile Cloud Computing (MCC) has become a pervasive technology that offers on-demand, flexible, and scalable computing resources to mobile devices. However, the security issues associated with MCC have become a major concern for users and organizations, leading to the development of various Security Modules. These modules typically use delegation methods that involve the transfer of data or operations from mobile devices to the cloud to perform the task at the best performance and security levels. Despite extensive attempts to design secure security modules of delegation in Mobile Cloud Computing (MCC), none of the existing modules possess all the necessary development attributes. Our analysis indicates that previous studies have not used security development attributes as evaluation criteria to compare and assess the available Security modules of delegation methods in MCC. However, Modeling these modules is critical and poses significant challenges in selecting the most secure security module. du to multicriteria, importance of data and data variation. To address this issue, this study proposes a Decision Modelling Approach for Security Modules of Delegation Methods in Mobile Cloud Computing using multi-criteria decision-making (MCDM) methods. The proposed approach involves the integration of Evaluation based on Distance from Average Solution (EDAS) method with fuzzy weighted with zero inconsistency (FWZIC) under Probabilistic Interval Neutrosophic Hesitant Fuzzy Set (PINHFS) environment. . The framework presented in this study involves two primary stages : the construction of decision matrices for Security Modules of delegation methods in MCC and the application of the PINHFS-FWZIC method to determine the weight of the security evaluation criteria. The EDAS method is then employed to modeling the Security Modules of delegation methods in MCC based on the formulated decision matrices and criteria weight. The validation and evaluation of the proposed framework were conducted through model validation and decision evaluation procedures. Model validation involved sensitivity analysis and systematic ranking procedures . The benchmarking checklist was used to compare the results of the proposed framework with the existing approaches. Based on the findings, it can be concluded that the proposed framework can efficiently weight the security criteria and successfully rank Security Modules of delegation methods in MCC. The PINHFS-FWZIC method effectively handled the uncertainty and hesitancy of the decision-makers in assigning weights to the evaluation criteria. Overall, the proposed framework provides a useful benchmark for evaluating other Security Modules of delegation methods in MCC. It can aid decision-makers in selecting the most secure MCC delegation method system by providing a comprehensive evaluation and Modeling of the available Security Modules.
Factors Affecting Cybersecurity Behaviour in the Metaverse: A Hybrid SEM-ANN Approach Based on Deep Learning
(The British University in Dubai (BUiD), 2023-01) ALMANSOORI, AFRAH
Cybersecurity procedures and policies are prevalent countermeasures for protecting organizations from cybercrimes and security incidents. However, without considering human behaviours, implementing these countermeasures will remain useless. Cybersecurity behaviour has gained much attention in recent years. However, little is known concerning the factors that influence the cybersecurity behaviour of Metaverse users. Consequently, this research has three key objectives. A comprehensive systematic review is steered to address research gaps in the current literature on cybersecurity behaviour via the lens of information system models and theories to identify the most prevalent factors, theoretical models, technologies and services, and participants. The systematic review identified 2,210 empirical studies published on cybersecurity behaviour between 2012 and 2021. In line with the existing gaps found in the literature, this research, therefore, develops an integrated model based on extracting constructs from the Protection Motivation Theory (PMT), Health Belief Model (HBM), and Theory of Interpersonal Behaviour (TIB). An external factor, “trust”, is also incorporated in the model to understand better the factors affecting the cybersecurity behaviour in the Metaverse. The developed model was then evaluated based on survey responses from 531 Metaverse users in the United Arab Emirates who used the Metaverse for personal or professional purposes. The empirical data were analysed using a deep learning-based hybrid Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) Approach. The integrated model explained 66.1% of the total variance in cybersecurity behaviour. The hypotheses testing results reinforced most of the suggested hypotheses in the developed model. The sensitivity analysis results for the ANN model revealed that “cues to action” have the most considerable importance in understanding cybersecurity behaviour in the Metaverse, with 97.8% normalized importance, followed by habit (69.7%), perceived vulnerability (69.6%), self-efficacy (40.9%), and trust (27.2%). Theoretically, integrating the PMT, HBM, and TIB along with the external factor, “trust”, is believed to add a significant value to validating the three theories in general and the cybersecurity behaviour in specific. Practically, understanding the impact of security factors would assist in understanding the effect of security incidents on cybersecurity behaviour in the Metaverse. Furthermore, policymakers and regulators should pay attention to and analyse the present data privacy policies and legislation to create specific policies and regulations for using the Metaverse. Moreover, cybersecurity companies, system analysts, and developers can use the insights from the essential factors as a form of a lesson to the refinement of presently implemented solutions as well as the anticipation of new future technological advancements.
Examining the Factors affecting the sustainability of blockchain technology in higher education using a hybrid SEM-ANN approach
(The British University in Dubai (BUiD), 2022-10) AL SHAMSI, MOHAMMED SALEM MATAR
Blockchain technology has received considerable attention during the last few years. Blockchain technology permits the creation of a distributed record of a decentralized digital event in which data and related transactions are not controlled by a third party. However, little is known concerning what aﬀects their sustainable use for educational purposes. Consequently, there are three primary goals for this research. A systematic review is conducted to address the research gaps in the literature on Blockchain adoption from the lens of information system (IS) models and theories. The systematic review included empirical studies published between January 2010 and December 2021 on the topic of blockchain adoption. Among the 918 articles, 30 articles were critically analyzed based on the inclusion criteria. This research, therefore, develops a theoretical model based on extracting constructs from the protection motivation theory (PMT) and expectation conﬁrmation model (ECM) to understand the sustainable use of blockchain in higher education. The developed model was then tested based on data collected through an online survey from 374 university students in the UAE who used blockchain technology for educational purposes. The model was empirically validated using a hybrid structural equation modelling-artiﬁcial neural network (SEM-ANN) approach. The hypotheses testing results reinforced most of the suggested hypotheses in the developed model. The sensitivity analysis results for model 1 revealed that satisfaction has the most considerable eﬀect on the sustainable use of blockchain technology with 100% normalized importance, followed by perceived usefulness (58.8%), perceived severity (12.1%), and response cost (9.2%). Besides, the sensitivity analysis for model 2 showed that perceived usefulness has the most considerable effect on the sustainable use of blockchain technology with 100% normalized importance. However, expectation confirmation has 29.2%. Theoretically, integrating the PMT and ECM will add significant value to the validation of the two theories in general and the blockchain in specific. Practically, understanding the impact of security factors would assist in understanding the effect of security incidents on the sustainable use of technology in higher educational institutions. In addition, governments, academia, businesses, and individuals often tend to share resources over a distributed ledger secured by means of cryptography. Blockchain technology also helps in facilitating the traceable, secure, and verifiable exchange of educational data across institutions effectively. Methodologically, the use of SEM-ANN in validating such theoretical models is rarely used, and hence, it would add value to the existing literature by measuring the non-linear relationships among the factors.
Examining the factors affecting users’ cybersecurity behaviour in mobile payment contactless technologies: A hybrid SEM-ANN approach
(The British University in Dubai (BUiD), 2022-12) ZAINAL, HANA YOUSUF
Modern businesses have adopted a plethora of advancements due to the emergence of digital transformation where the recent lifestyle requires to have a robust platform to process all the communication in an effective and secure way. Advanced technologies adopted by the society purely concentrated on the software and cybersecurity enhancements from the software perspective ignoring the fact that a human behaviour can be the most major and severe threat for these advanced technologies. The adoption of any new technology in the society has been turned into a must rather than a complimentary due to the rapid growth of the technology advancements and people curiosity to investigate new trends. Technology adoption depends on the behaviour of the end users in dealing with the technology. Mobile payment contactless technologies are one of the fast-growing technologies specially after the appearance of the COVID-19 where all the payments and transactions are performed using RFID (Radio-Frequency Identification) or NFC (Near Field Communication) techniques. The individual’s cybersecurity behaviour is a major concern in these technologies. A quick review of literature showed that none of the studies discussed the individual’s cybersecurity behaviour toward this technology. Therefore, this research has three main objectives. First, conducting a systematic of systematic reviews on cybersecurity through a multidisciplinary perspective to address the research gaps in the existing literature of human cybersecurity behaviour. The systematic review of systematic review included empirical studies published between January 2015 and February 2022 on the topic of cybersecurity. A total of 794 studies have been reviewed, and 60 studies were found qualified and considered in the analysis. Through the analysis, little attention has been paid to cybersecurity behaviour in general and mobile payment contactless technologies in specific. Moreover, the existing literature discussed the utilization of mobile payment contactless technologies in terms of adoption and use in a certain criteria or environment and do not measure or explore the cybersecurity behaviour towards this technology. To bridge this literature gap, this research builds an integrated theoretical model by combining factors from the Protection Motivation Theory (PMT), Technology Threat Avoidance Theory (TTAT), Theory of Planned Behaviour (TPB), along with cybersecurity awareness to measure the cybersecurity behaviour in mobile payment contactless technologies. The model was then empirically validated using a hybrid Structural Equation Modelling-Artificial Neural Network (SEM-ANN) approach based on data collected through questionnaire surveys from mobile payment contactless technologies users in the UAE. The results indicated that the attitude, subjective norm, cybersecurity awareness, perceived behavioural control and response efficacy have a significant influence on cybersecurity behaviour. On the other side, the results indicated that perceived threat, response cost, and self-efficacy have insignificant influence on cybersecurity behaviour. The model explained 53% of the total variance in cybersecurity behaviour. Additionally, the ANN results showed that cybersecurity awareness is the most important factor affecting cybersecurity behaviour, with a normalized importance of 100%. Theoretical contributions, practical implications, and limitations and future research were also discussed.
A Framework to Implement Advanced Technologies in Higher Education Institutions: A Blockchain-Based Solution Case Study
(The British University in Dubai (BUiD), 2022-11) AL MANSOORI, SUAAD
The main drive behind this research is a statement made by Haugsbakken and Langseth (2019) that higher education institutions (HEIs) have a history of being slow and ineffective in adopting new technologies. The United Arab Emirates has encouraged HEIs to adopt new technologies to reinforce services. One of the main operations in HEIs is managing students’ credentials which requires HEIs to implement smart solutions rather than following traditional practices such as filling paper-based forms and getting approvals via emails. However, smart solutions, technologies, and advanced systems are associated with several challenges that might drive decision-makers away from replacing the traditional practices with new advances solutions in HEIs. This research proposes a general implementation framework that can guide decision-makers in HEI when implementing new solutions and technologies. The research also takes Blockchain Technology as a case study to customize the proposed framework and use it to implement Blockchain-based solutions. The researcher has conducted several qualitative and quantitative methods. First, highlighting challenges of implementing new solutions that were discussed in literature; with the focus on Blockchain as one of the most promising technologies. Second, building an online survey questionnaire in which HEIs stakeholders participated in to rank the challenges and provide further insights. Third, studying the case of a private university in The United Arab Emirates to highlight the issues of the current traditional credentials management practices and use the new proposed framework to implement a new Blockchain-based system in the same university. For the validation, the research was presented and published at two international conferences. It was also validated with the experts’ feedback and a live demo. This research does not focus on the technical aspects of Blockchain technology or any other technology but serves as a guideline to motivate decision-makers and stakeholders in HEIs to adopt new technologies.
Federal Multi Criteria Decision Making Framework in Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients as Case Study
(The British University in Dubai (BUiD), 2022-06) ALSEREIDI, ABEER
No specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) when the epidemic firstly broke out. The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, it is a promising treatment for mild-to-moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing; the second is the prioritisation of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research has combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, which indicates a research gap. This thesis presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 according to medical requirements by using a novel federated decision-making distributor (FDMD). A novel FDMD of anti-SARS-CoV-2 mAbs is proposed for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC) and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritisation of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritisation processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. The results are presented as follows: (1) The proposed FDMD efficiently and independently identifies the high-risk patients who are most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weights the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritisation ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. The study of the proposed FDMD implies that it has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritised according to their eligibility and dose availability. It also simultaneously protects their privacy and offers an effective cure to prevent progression to severe SARS-CoV-2, hospitalisation or death.
NOVEL STACKING CLASSIFICATION AND PREDICTION ALGORITHM BASED AMBIENT ASSISTED LIVING FOR ELDERLY
(The British University in Dubai (BUiD), 2022-09) JINESH, PADIKKAPPARAMBIL
The ageing of the population in developed nations necessitates the expansion of medical services, raising the cost of both economic and human resources. In this respect, Ambient Assisted Living (AAL) is a comparatively new information and communication technology (ICT) that delivers services. It acknowledges numerous products that help elderly and disabled people to live autonomously and enrich the quality of their lives. It also aids in the cost-cutting of hospital services. Various sensors and equipment are installed in the AAL context to collect a wide variety of data. Furthermore, AAL could be the motivating technique for the most recent care models by working as an adjunct. Research and development (R&D) projects and business activities in AAL and smart home contexts frequently emphasize the significance of technology. While ICTs promote health care, they have the potential to alleviate loneliness and social isolation among the elderly. They help to enhance, expand, and maintain the social interactions of the elderly while also increasing the individual's emotional well-being. The emergence of smart homes will help the elderly and the disabled live better lives. Over the past five years, the acceptance of wearable fall detection technologies has increased. These techniques involve calling for help in an emergency, such as falling or being immobile for long periods. Because falling is widespread among older adults, it can have serious health consequences. Falls can result in physical traumas such as fractures, head injuries, and severe decay. Falls will have a considerable impact on some populations, necessitating the development of better fall prevention and management solutions. Therefore, this thesis proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based on AAL for the older people with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. The primary objective of this thesis is to identify the fall detection and prediction in older persons, such as 0 - no fall detected, 1 - person slipped/tripped / fall prediction, and 2 - definite fall. This study's dataset was sourced from the Kaggle machine learning repository, and it refers to data gathering from wearable IoT devices. The experimental outcomes demonstrate the proposed MCFS, NCA, and NSCP algorithms work more effectively than previous feature selection, clustering and classification algorithms, respectively, in terms of accuracy, sensitivity, specificity, precision, recall, f-measure and execution time. This thesis concludes with a discussion of future work to improve the proposed methodology and future research directions.
Digital Forensics Framework for Investigating Hyperledger Fabric Blockchain Networks
(The British University in Dubai (BUiD), 2022-03) AL BARGHUTHI, NEDAA BAKER JAMIL
Industry leaders worldwide are adopting a new initiative to replace all paper transactions with digital ones and adopt them on blockchain platforms. Many pilot projects rely on private blockchain platforms such as the Hyperledger Fabric platform, whether at the government or private sector level. The increasing rise of this type of platform has led to the new research approach in digital forensics. There is a lack of research papers dealing with digital forensic techniques within the Hyperledger platform. This research fills the gap in the literature and provides guidelines for investigating the blockchain network. It also provides an approach for digital forensic investigators to examine the layers and associated components of Hyperledger Fabric, involving either criminal incidents, violations or abuse. This prototype retrieves relative evidence and contributes a valuable model to the current state of research. This research focuses on four objectives. proposing a prototype for network traffic investigations and retrieving all possible clues from the Hyperledger Blockchain. In addition, the research developed data mining methods and techniques to analyse the BC components. These techniques use a deployed Python-based Forensics Extractor Tool to automate the process. Furthermore, the criminological method was introduced and offered a novel and a law enforcement approach to investigate legal criminal cases in HLF BC networks. These objectives follow an adequate assessment of the characteristics of the main network components and examine in-depth network traffic. As a result, this research overcomes these challenges by working on flexible and reliable digital investigations. The numerical data extracted from the proposed framework has been verified and validated using various samples to test this framework. Accordingly, all research hypotheses were fulfilled during the research study. The outcome of the research is promising. and excellent results were illustrated. Moreover. this proposed prototype serves as a general framework for in-depth monitoring and analysis of the Hyperledger Blockchain network.
Towards the development of an integrated model for examining the determinants affecting the use of Queue Management Solutions in Healthcare
(The British University in Dubai (BUiD), 2021-10) ALQUDAH, ADI AHMAD ALI
Over the years, long queues were recognized as a common problem in the healthcare domain, and it is significant to manage them for patients' safety and overall satisfaction. Prolonged queues in healthcare organizations can produce high levels of distraction for the employees instead of focusing on their original activities. As a solution, queue management technologies became more popular in healthcare organizations to solve queue issues, gather data, and generate statistical reports for the current and future flow trends. The adoption of new technologies in healthcare has been turned into a must rather than a luxury due to the rapid changes of technology advancements and people's needs. In general, the success of technology adoption in healthcare relies on the behavior of end-users towards accepting and using the technology. Queue management solutions (QMS) face resistance from users, and their acceptance is not assured. A quick review of the literature showed a lack of studies that discuss the acceptance of QMS. Therefore, this research has three main objectives. First, conducting a systematic review to address the research gaps in the existing literature and understand the extensively utilized acceptance models in healthcare and their related constructs. The systematic review included empirical studies published between January 2010 and December 2019 on the topic of technology acceptance in healthcare. A total of 1,768 studies have been reviewed, and 142 studies were found eligible and considered in the analysis. Through the analysis, the technology acceptance model (TAM) and the Unified theory of acceptance and use of technology (UTAUT) have been recognized as the prevailing models in technology acceptance in healthcare. Additionally, 11 factors from various acceptance models were found extensively investigated to understand and analyze the technology acceptance in the healthcare domain. These factors include Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Attitude Towards, Behavioral Intention, Use Behavior, Computer Anxiety, Computer Self-Efficacy, Innovativeness, and Trust. In line with the gaps found in the literature, this research has presented a case study for the currently implemented queue management solution (QMS) in the out-patient department (OPD) in a healthcare organization in UAE. The research discussed the suggested business and technical optimizations that include integrating the QMS with the electronic medical records solution (EMR). The integration was achieved using Health Level Seven (HL7) integration standards, including the exchange of custom-designed XML and HL7 messages. The goal of the integration was to implement a novel tool for patient’s self-check-in and enhance the ease of use and usefulness of QMS. As a pilot implementation, the feasibility of the newly implemented tool was assessed through a simulation experiment in the internal medicine clinic over two different weeks (control and intervention). A total of 127 appointments were identified as eligible and included in the study. The patient’s journey was split into five stages: identification, wait to triage, triage process, wait to treatment, and treatment process. The results revealed that the new tool is beneficial, and the median times to finish the processes within the patient’s journey have significantly decreased. To evaluate the use of the enhanced QMS, this research develops an integrated model based on the integration of various constructs extracted from different theoretical models, including the UTAUT, TAM, and social cognitive theory (SCT) along with trust and innovativeness as external factors. The model was empirically validated using the partial least squares-structural equation modelling (PLS-SEM) approach based on data collected through a questionnaire survey from 242 healthcare professionals. In brief, the results exposed that the suggested model can be helpful to explore the acceptance of information technologies in healthcare. The model has explained 66.5% of the total variance in the behavioral intention to use the enhanced QMS, along with 59.3% of the total variance for the actual use of the enhanced QMS. The results indicated that innovativeness and computer self-efficacy factors have a positive significant influence on the effort expectancy of professionals to use QMS. The computer anxiety factor has a negative significant influence on the effort expectancy to use QMS. Besides, trust and computer self-efficacy factors have a positive significant influence on the performance expectancy when using QMS. Other results, related implications, limitations, along with future research were also clarified and discussed.
Dynamic Cyber Resilience of Interdependent Critical Information Infrastructures
(The British University in Dubai (BUiD), 2021-12) JUMA, MAZEN GHAZI
We are becoming progressively reliant on the Critical Information Infrastructures (CIIs) to provide essential services in our daily lives, such as telecommunications, energy facilities, financial systems, and power grids. These interdependent infrastructures form one coupled heterogeneous network that qualifies them to deliver new cyber roles and crucial tasks not achievable before in numerous domains worldwide. The CIIs have to deal with sophisticated cyber risks resulting from cyber vulnerabilities of their scale-free topology targeted by different cyber threats like concurrent and consecutive cyberattacks to the expected failure cause of the single hub nodes in their decentralized structures lead to cascading and escalating cyber failures that interrupt the vital services and considerable losses in modern societies with vast negative impacts on the economy and national security. Therefore, the research community has attempted over the last decade to pay attention to address the cyber protection gaps of CIIs in many studies by enhancing the existing standard solutions based on cyber trustfulness engineering, for example, distance-vector, link-state, and path-rule solutions, or developing new ones, but still missing one comprehensive technology solution. The required solution has to bridge the current literature gaps by shifting the paradigm of cyber CIIs protection properly towards dynamic cyber resilience to balance proactive and reactive perspectives at theoretical and empirical levels. Besides, it also needs to understand, analyze, evaluate, and optimize the set of dynamic cyber resilience capabilities consisting of withstanding, mitigation, recovery, and normalization. These capabilities support the various states of the typical cycle of dynamic cyber resilience, including threshold, bottom, and equilibrium states to increase CIIs robustness against cyberattacks, absorb frequent cyber disturbances that occurred, recover quickly from cyber failures, and re-establish their acceptable performance levels within appropriate timeframe. This thesis presents the novel proposed solution of dynamic cyber resilience using cyber zero-trust engineering for the first time to cope with highlighted shortcomings of the standard solutions, overcome the single hub node failure and enhance dynamic cyber resilience capabilities of interdependent CII networks against concurrent and consecutive cyberattacks to deliver their core services continuously. The research goal of this thesis was accomplished by an iterative four-objective cycle through two phases: primary and optimization. In the primary phase, the novel conceptual framework of the proposed solution was developed based on four fundamental concepts: decentralized registry, delegated peers, consensus rules, and dynamic routing. The technology stack of the proposed solution was also implemented with four algorithms and eight protocols. The evaluation results of the proposed solution were compared to the results of standard solutions under different cyberattack scenarios using quantitative research methods involving computing simulations, emulation experiments, and analytical modeling. The optimization phase improved the conceptual framework by adding three new fundamental concepts: hubs coupling, encrypted transmission, and end-to-end service quality. The technology stack was also enhanced with three new algorithms and five protocols. The proposed solution was optimized using the iterative four-objective cycle based on previous primary phase results. Lastly, all results in both phases were analyzed and discussed, and the final findings of the thesis were interpreted. However, it can be concluded that the proposed solution failed to compete with other standard solutions in terms of dynamic cyber resilience capabilities and total resilience measurements during the primary phase. Nevertheless, the optimized solution achieved the optimal results compared to the standard solutions. Finally, study limitations and recommendations for future works represented the research outcomes and contributions.
A Cybersecurity Skills Performance Dashboard (CSPD): The Use of a Technical Gamification Simulation Platform (TGSP) to Address Cybersecurity Skills Gap in the UAE
(The British University in Dubai (BUiD), 2021-10) BAZARGAN, FATMA AHMAD
Cybersecurity capacity building has been one of the main pillars of the UAE cybersecurity strategy to shorten the cybersecurity skills gap in the UAE. There have been several capacity building initiatives introduced by the UAE government to upscale the hands-on technical skills amongst cybersecurity professionals in the UAE. However, there has not been any mechanism nor a platform in place to either measure the scale of the skills gap that currently exist and needs to be addressed nor any measurement tool to provide visibility into the effectiveness of the capacity building initiatives introduced. Furthermore, there have been a plethora of undergraduate cybersecurity academic degree programs taught in various recognized educational institutions across the UAE. However, the shortage of cybersecurity practitioners globally and locally has never been more acute. Hence, there is a crucial need to introduce a UAE nation-wide cybersecurity skills performance dashboard that shall provide the required visibility into the scale of the skills gap that currently exists, hands-on technical skills that is currently available, and those skills that need to be developed and trained. The Cybersecurity Skills Performance Dashboard (CSPD) shall provide the true measurement of the current existent skills gap shortage in the UAE to enable the concerned entities to introduce the needed capacity building initiatives to shorten the skills gap. In addition, there is a heightened need to introduce new ways of instructing cybersecurity academic program to upscale the hands-on technical cybersecurity skills amongst university undergraduate students and thus improve a variety of technical skills such as digital forensics, incident response, reverse engineering, cryptography, penetration testing, and many more. This research study aims to design and develop the Cybersecurity Skills Performance Dashboard (CSPD) as a measurement tool for capacity building of cybersecurity professionals. It records and provides a true assessment of the upscale of hands-on technical cybersecurity skills of cybersecurity professionals. This is fulfilled through the introduction of new ways to the current traditional academic teaching model through the use of the Technical Gamification Simulation Platform (TGSP) to shorten the cybersecurity skills gap problem in the United Arab Emirates. Integrating the TGSP within the fabric of the undergraduate cybersecurity academic programs will produce market-ready professionals to fill in the active cybersecurity job postings in the United Arab Emirates or globally. This study employs a qualitative grounded theory research design to understand and explore how the use of the technical gamification simulation platform will enhance the hands-on technical skills amongst undergraduate students. Purposeful sampling was used to mindfully select the sample for this research work from a pool of 3rd and 4th-year undergraduate students majoring in cybersecurity from various renowned universities across the United Arab Emirates. This research work that applied to the selected participants consisted of three phases: assess, train, and perform. The data was collected from all the three phases. The first phase, the assessment phase, was in the form of responses to one-on-one interview questions. The second phase, which was the training phase, was in the form of results collected from the cybersecurity skills performance dashboard that was designed and developed by the researcher. Finally, the performance phase involved results that were in the form of responses to the post-training survey questionnaire by the participants in the study. Analysis and findings indicated that the undergraduate students found the technical gamification simulation platform an invaluable tool to upscale their hands-on technical skills because it provided them with a simulated real-world environment whilst using real-world tools to complete the technical scenario in a structured manner. This research work is considered to be the first to be conducted in the United Arab Emirates that examined providing undergraduate students majoring in cybersecurity with access to the technical gamification simulation platform for a duration of 8-weeks. Also, it was able to draw invaluable information on the effectiveness of introducing the training platform alongside the academic curriculum to upscale the hands-on technical skills. Several research questions were tested as part of this research work within the U.A.E. context. Finally, this thesis delivers the design and development of the cybersecurity skills performance dashboard (CSPD) as a measurement tool for capacity building of cybersecurity professionals and a contribution to this research study. The CSPD captures and displays the scores of the students as they complete a given technical scenario or challenge in the technical gamification simulation platform. Hence, the dashboard provides a true assessment of the cybersecurity undergraduate’s technical hands-on skills. The CSPD provides the required means to various entities (i.e., government entities, private sector, business owners, etc.) to approach the cybersecurity professional based on the skills they most need through the use of CSPD. The beneficiaries of this dashboard include entities in the UAE and worldwide. In addition, the dashboard can be used as a reference by the U.A.E. cybersecurity policymakers to understand the cybersecurity skills that are widely available in the United Arab Emirates and the skills that further need to be trained and developed. Hence, being able to tailor the capacity building campaigns on factual data. Also, the dashboard can act as a nationwide cybersecurity skills performance database/repository in the United Arab Emirates to understand the current availability of cybersecurity professionals and talents. Although the dashboard in this research work is applied to the field of cybersecurity. However, it can be generalized and applied to any other field of expertise to gain invaluable insights into the skills gap.
Artificial Intelligence Frameworks for Sentiment Variations’ Reasoning and Emerging Topic Detection
(The British University in Dubai (BUiD), 2021-07) ALATTAR, FUAD ABDELWAHAB ABDELQADER
Utilizing Sentiment Analysis techniques to monitor public opinions on social media has been an essential yet challenging task in the field of Artificial Intelligence (AI). Many studies were conducted during the last two decades to help users tracking public sentiments about entities, products, events, or other targets. However, these techniques focus on extracting overall positive/negative/neutral polarity of texts without identifying the main reasons for extracted sentiments. This thesis contributes to the very few studies that took one step ahead by developing novel models to understand what causes sentiment changes over time. Obviously, identifying main reasons for public reactions is valuable to decision-makers so that they can take necessary actions in a timely manner. To develop our approach, we first examined existing Sentiment Reason Mining methods to identify their limitations, then we introduced our Filtered Latent Dirichlet Allocation (Filtered-LDA) Model that overcomes major deficiencies of base methods. This model can be used for multiple applications, including detection of new research trends from large sets of scientific papers, discovery of hot topics on social media, comparison of customer reviews for two products to identify their strong/weak aspects, and our focus topic of interpreting public sentiment variations. The Filtered-LDA Model utilizes a novel Emerging Topic Detection technique for which we developed multiple AI frameworks. It emulates human approach for discovering new topics from a large set of documents. A human would first skim through all old and new documents to isolate the new ones that may contain Emerging Topics. These clustered documents are then analyzed to identify the high-frequency emerging topics. With this simple method, the impact of clustering errors is significantly reduced as the wrongly clustered documents do not usually contain main keywords of high-frequency emerging topics. Furthermore, the new frameworks introduce measures to genuinely reduce chances of detecting old topics and visualize candidate reasons online. Given that some social media platforms, like twitter, use short-text documents, we first compared accuracies of state-of-the-art Sentiment Analysis classifiers to select the best performer for short-texts. Subsequently, the selected classifier is applied on a real-life large Twitter dataset, which includes around two million tweets, to extract positive/negative/neutral sentiments. The Filtered-LDA Model is tested first on a Ground Truth dataset to validate that it outperforms baseline models, then it is finally applied on the large Twitter dataset to automatically conclude main reasons for sentiment variations
Customs Trade Facilitation and Compliance for Ecommerce using Blockchain and Data Mining
(The British University in Dubai (BUiD), 2021-07) Alqaryouti, Omar
Electronic commerce (ecommerce) has penetrated every society, organization, business and household and changed consumers’ habits. It enabled businesses in some nations to trade beyond local borders and reach global proportions. This led to the explosive growth in demands for ecommerce platforms over the last few years and the increased popularity in cross-border trade interactions. The popularity became more evident in times of crisis such as COVID-19 for critical food and medical supplies and products. However, it was disrupted in other markets due to societies going on lockdown, which were further accentuated by borders being shut down. Ecommerce cross-border trade is impacted by regulations of each country. The challenges facing global trade and Customs administrations in particular cover many dimensions. Customs being tasked with protecting society and the smooth trade flow can no longer rely on traditional practices. A coordinated and consorted effort is required to disrupt illegitimate activities and support the mission of Customs. This work first aims to determine factors that drive the adoption of Blockchain technology. Blockchain is characterized for providing visibility, integrity, provenance and immutability across participants through the shared ledger capabilities. Therefore, blockchain technology is used in this study to build a framework to enhance trade facilitation and increase compliance while eliminating risks. This framework will provide advance access to information from various sources and will enable real-time discovery of risks. Accordingly, two off-chain clustering algorithms are proposed to determine value manipulation in ecommerce transactions and increase the efficiency of Customs Audit process. The Software Development Life Cycle (SDLC) methodology is adopted to build the framework. An integrated web application is developed to mock up the end-to-end process in ecommerce. Additionally, the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology is employed for modelling the two proposed clustering algorithms to identify transactional risks. The usability of the proposed framework is evaluated using the System Usability Scale (SUS) resulting in overall high acceptability levels across all users. Furthermore, accuracy measures are used to evaluate performance of the proposed clustering algorithms, reaching 86% for valuation assessment and 87% for risk identification in customs audit. The proposed framework will revolutionize the way trade supply chain is handled. It will create a shift from reactive limited visibility to proactive full visibility mode and properly manage various scenarios such as the current health hurdles and any future challenges lurching around.

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