Books by Daniyal Alghazzawi
Malware - Detection and Defense [Working Title], Intechopen, 2023
Although unmanned aerial vehicle (UAV) has found many applications in various fields, its operati... more Although unmanned aerial vehicle (UAV) has found many applications in various fields, its operation has been constrained by its low flight endurance. To date, several design efforts are pursued to improve this performance and one of them is the exploration of blended-wing-body (BWB) design. In this study, parametric study is conducted on the BWB UAV design of Baseline-VII that is developed by Flight Technology and Test Center (FTTC), Universiti Teknologi MARA Shah Alam, Malaysia. The primary goal is to optimize the current Baseline-VII design for maximum lift-to-drag ratio, which in turn implies a higher flight endurance. Three design parameters are considered: inboard wing sweep angle, outboard wing sweep angle and also inboard wing span. A full-factorial design of experiments (DoE) is applied to set the total 27 design case settings for this study, with three different values considered for each design parameter: 10°, 25° and 50° for inboard and outboard wing sweep angles, and 200 mm, 300 mm and 400 mm for the inboard wing span. The computer-aided design (CAD) models for the design cases are constructed using Solidworks and the resultant aerodynamic lift-to-drag ratio is found through computational fluid dynamics (CFD) simulation analysis using ANSYS Fluent. The collected data is then statistically analysed using regression analysis in MINITAB to construct a representative regression model that aptly capture the effects of the varied design parameters on the design aerodynamic lift-to-drag ratio. Based on the results, it has been found that the maximum lift-to-drag ratio for the modified Baseline-VII UAV design is 2.8119, which is obtained with optimal settings of inboard wing sweep angle = 17.2727°, outboard wing sweep angle = 20.9091° and inboard wing span = 400 mm. This is about 28.4% increment of lift-to-drag ratio from the original Baseline-VII design.
Sensors, 2022
Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical app... more Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels
Education in future implies a reconstruction in the education system. This practically means impl... more Education in future implies a reconstruction in the education system. This practically means implementation of reform of the entire educational system and development of conception of the permanent education accorded with social needs and changes. The presented work here is an integral part of a broader set up and realized preliminary project on "Redefinition of Education Structure of Republic of Serbia" forwarded to the Ministry of Education of the Republic of Serbia in 2010. The preliminary project guided by Ph.D. Miroslav Kuкa and Ph.D. Vuкosava Zivкović was realized in the team work and in coordination of work of the central and regional working groups in Serbia and the surrounding countries having 80 collaborators in total working on the project. Model of our structure of the education system extends the period of compulsory education up to 10 years of age (till the first grade of high school which is the same for all regarding the curriculum) and is based on differentiation of the education levels (from preschool to high school) in cycles, which, on their part, are defined by aims and tasks. Short-term, middleterm and long-term aims have been clearly defined and concise division of competence and the follow-up methods of successfulness of its implementation has been made within the proposal of our model.
Genes, 2019
Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There h... more Self-interacting proteins (SIPs) is of paramount importance in current molecular biology. There have been developed a number of traditional biological experiment methods for predicting SIPs in the past few years. However, these methods are costly, time-consuming and inefficient, and often limit their usage for predicting SIPs. Therefore, the development of computational method emerges at the times require. In this paper, we for the first time proposed a novel deep learning model which combined natural language processing (NLP) method for potential SIPs prediction from the protein sequence information. More specifically, the protein sequence is de novo assembled by k-mers. Then, we obtained the global vectors representation for each protein sequences by using natural language processing (NLP) technique. Finally, based on the knowledge of known self-interacting and non-interacting proteins, a multi-grained cascade forest model is trained to predict SIPs. Comprehensive experiments were performed on yeast and human datasets, which obtained an accuracy rate of 91.45% and 93.12%, respectively. From our evaluations, the experimental results show that the use of amino acid semantics information is very helpful for addressing the problem of sequences containing both self-interacting and non-interacting pairs of proteins. This work would have potential applications for various biological classification problems.
"My major is Computer Science. One day, I had a question about a technical issue which was about ... more "My major is Computer Science. One day, I had a question about a technical issue which was about hacking. I was looking for the fastest way to find the answer through the Internet. I ended up with tens of questions instead of just one. In addition to these questions, I still did not get the answer for my main question because some websites do not represent the knowledge in a clear way. Therefore, I had to work since that time on these kinds of websites and try to find steps that they need to follow in order to help them present the knowledge in a clear way.
On January 03, 2002, “the FBI’s NIPC (National Infrastructure Protection Center) had strongly recommended that all users should immediately disable windows’ Universal Plug and Play support”. Then, GRC (Gibson Research Corporation) released a small tool to disable this service. "Universal Plug and Play" is one of the Windows’ services that opens a backdoor in your computer. There are more than 100 services that come with Microsoft Windows 7, and these services are under the Administrator Tools, so most people do not like to play with them. Therefore, GRC released a tool just to disable this specific service. Number of companies built number of tools to help you configure these services without the need to do it by yourself because they want to prevent users from playing with this technical complex issue. In my opinion, this technical issue is complex because they made it to be complex for us.
Therefore, I decided to take "Microsoft Windows Services" as an example in my book. I moved all the content from the tool developed by Microsoft to a website. Then, I applied number of steps, called the GOALAPE Model, on the website in order to get a comprehensible website. At the end, a survey has been conducted on a group of college students to measure their understanding of the original tool developed by Microsoft and my new website."
The design and implementation of e-Learning platforms is essential for the development and future... more The design and implementation of e-Learning platforms is essential for the development and future of information and communication technologies in knowledge management in the teaching/learning process. Universities and companies require a methodology for developing versatile and flexible e-Learning applications that are, at the same time, capable of storing the large volumes of information required by these educational processes and efficiently conveying this information to their users. This situation is a catalyst revealing the vital need for the efficient and timely development of a teaching/learning process based on e-Learning platforms that takes into account the needs of the student/teacher and achieves optimum quality. To achieve this goal a methodology is required that standardizes the conception, design and implementation of this type of systems based on the creation of basic artefacts that can be used equally well across the different platforms developed. The methodology proposed should be based on a systematic approach for the development of e-Learning systems considering systematic methods coming from both e-Learning and software development communities, involving a series of stages each containing work flows and phases and a set of artefacts (cards, reports, templates, etc.) that can form the basis of the design and development of any e-Learning platform. By doing so, we aim at the development of, what we have named, a Model-Based Instructional System Development Environment (Mb-ISDE), to include e- Learning development in the current trends of model-based software development.
Papers by Daniyal Alghazzawi
Sensors, 2024
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
IEEE, 2024
The exponential growth of the Internet and the widespread availability of personal data have rais... more The exponential growth of the Internet and the widespread availability of personal data have raised significant concerns regarding personal privacy and societal security. In response, the “right to be forgotten” has emerged as a fundamental principle, empowering individuals to request the removal of their personal data. Though relieving the privacy concerns at a certain degree by doing so, Artificial Intelligence (AI) models, trained by personal data, still suffers from privacy leakage risks. Therefore, machine unlearning attracts great attention from both industry and academia, aiming at erasing the risks of privacy leakage of AI models. However, existing machine unlearning methods primarily operate within centralized frame-works, limiting their applicability to decentralized Federated Learning (FL) settings where data is decentralized and not directly accessible. Additionally, current federated unlearning methods only approximate data deletion, leaving a potential risk of data leakage. To address these issues, this paper presents FedCSA, The FedCSA approach adopts a divide-and-conquer strategy by clustering and slicing the users’ data. Subsequently, submodels are trained on each individual slice, and their aggregation yields the global model. During the unlearning task, only the corresponding sub-model undergoes retraining, significantly reducing training time while ensuring data privacy and achieving precise data deletion. Extensive experiments are conducted on real-world datasets, including MNIST, Fashion-MNIST, and CIFAR-10, to evaluate the performance of FedCSA. The results demonstrate that FedCSA outperforms state-of-the-art machine learning methods, such as retrain and SISA, in terms of efficiency and global model performance. Our code is publicly available at https://github.com/ZhenWang9/FedCSA.
Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, 2024
Protein function prediction has long been a widely discussed task in the field of synthetic biolo... more Protein function prediction has long been a widely discussed task in the field of synthetic biology, and it is of paramount importance for gaining a deeper understanding of the roles and interactions of proteins within living organisms. Since the 3D structure data of proteins obtained experimentally are far less in quantity than the corresponding protein sequence data, most experiments related to protein function prediction currently rely on using protein sequences as training data, although 3D protein structures contain much more information. Here, an enzyme turnover number prediction model (PSKcat) is proposed based on 3D protein structures. PSKcat takes protein PDB files as input, represents proteins using a modified pre-trained model called GearNet-Edge for 3D protein structures, and combines graph neural network to characterize the substrates involved in enzyme reactions. In order to verify the effectiveness of the model, several enzyme reaction datasets were constructed, and multiple groups of comparative experiments were conducted. The experimental results demonstrate the feasibility of using 3D protein structures for enzyme function prediction, which opens up avenues for further exploration of the applications of 3D protein structures in the future.
Neurocomputing, 2024
In the field of protein engineering, the function and structure of proteins are key to understand... more In the field of protein engineering, the function and structure of proteins are key to understanding cellular mechanisms, biological evolution, and biodiversity. With the advancement of high-throughput sequencing technologies, we have accumulated a vast amount of protein sequence data, yet the protein properties and functional information contained within these data have not been fully deciphered. Predicting protein properties is crucial for revealing how proteins function within complex biological systems and also offers possibilities for the early diagnosis of diseases and the development of new drugs. However, due to the complexity of protein properties and functions, traditional experimental methods face significant challenges in terms of cost, time, and accuracy. In recent years, machine learning techniques have become a powerful tool for addressing these challenges due to their ability to learn patterns and relationships from large-scale data. Machine learning methods have demonstrated outstanding performance in areas such as protein structure prediction, function annotation, interaction recognition, and physicochemical property prediction. This survey reviews the application of machine learning in protein property prediction. Current research progress, challenges in the field, and future development directions have been discussed, highlighting the significance and potential of machine learning methods in advancing protein science research and applications.
Neurocomputing, 2024
Bioinformatics is a subject that studies life phenomena by using mathematical and information sci... more Bioinformatics is a subject that studies life phenomena by using mathematical and information science theories and techniques. Its main tasks, such as DNA sequence comparison, protein structure prediction and cell metabolism analysis, can be regarded as complex optimization problems with different characteristics. Evolutionary computing is a kind of global optimization algorithm inspired by nature. Over the years, scholars have accumulated fruitful results in solving complex optimization problems such as large-scale, dynamic, multi-modal, multi-objective and multi-constrained problems by using Evolutionary Computation algorithms, and have successfully applied to the above optimization tasks in bioinformatics. This paper mainly summarizes the work of Evolutionary Computation technologies in bioinformatics from 2019 to 2023 at multiple levels, including Genomics, Proteomics, metabolomics and molecular networks related optimization tasks, as well as further applications in disease diagnosis and drug development.
IET Signal Processing, 2024
Wireless communication plays a crucial role in the automation process in the industrial environme... more Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.
Sensors, MDPI, 2024
Wireless physical layer authentication has emerged as a promising approach to wireless security. ... more Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate.
IEEE Internet of Things Journal, 2023
Nowadays, the Internet of Things (IoT) has become immensely popular in various fields like health... more Nowadays, the Internet of Things (IoT) has become immensely popular in various fields like healthcare, smart cities, and industrial automation. IoT networks are expanding rapidly, including different IoT devices with limited capabilities in terms of power and storage which make the IoT security a crucial issue. IoT Network Intrusion Detection System is one of the most famous solutions that used to identify different types of attack and extract their features (e.g. IP addresses of attackers). The IP address is a valuable feature that can identify malicious traffic of an attacker who attempts to access the IoT network. However, IoT Network Intrusion Detection Systems has different limitations: centralization and scalability which easily allow attackers to access the IoT network. Accordingly, this paper aims to address these issues by proposing a novel collaborative framework called Blockchain-based Collaborative Intrusion Detection Systems (BC-IDSs) that utilizes Blockchain technology to connect several IDSs. The BC-IDSs framework (1) creates a list of malicious IP addresses using IDSs; (2) utilizes Blockchain to share and store the Blacklist; (3) creates a function for duplication check in the Blockchain layer. Further, the implementation of a proof of concept for BC-IDSs framework is presented by using Ethereum Blockchain simulators. Compared to previous works, this paper discusses several types of performance metrics that prove BC-IDSs is able to secure IoT networks. BC-IDSs also increases the scalability by 50% when compared to one of the previous defence work.
Digital, 2023
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Enhancing Cyber Security Governance a... more first_pagesettingsOrder Article Reprints
Open AccessArticle
Enhancing Cyber Security Governance and Policy for SMEs in Industry 5.0: A Comparative Study between Saudi Arabia and the United Kingdom
by Nisha Rawindaran 1,Liqaa Nawaf 1,*ORCID,Suaad Alarifi 2,Daniyal Alghazzawi 2ORCID,Fiona Carroll 1ORCID,Iyad Katib 2ORCID andChaminda Hewage 1ORCID
1
Cardiff School of Technology, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
2
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Digital 2023, 3(3), 200-231; https://doi.org/10.3390/digital3030014
Received: 13 June 2023 / Revised: 19 July 2023 / Accepted: 31 July 2023 / Published: 14 August 2023
Download Browse Figures Review Reports Versions Notes
Abstract
The emergence of Industry 5.0 has revolutionized technology by integrating physical systems with digital networks. These advancements have also led to an increase in cyber threats, posing significant risks, particularly for small and medium-sized enterprises (SMEs). This research investigates the resistance of SMEs in Saudi Arabia and the United Kingdom (UK) to cyber security measures within the context of Industry 5.0, with a specific focus on governance and policy. It explores the cultural and economic factors contributing to this resistance, such as limited awareness of cyber security risks, financial constraints, and competing business priorities. Additionally, the study examines the role of government policies and regulations in promoting cyber security practices among SMEs and compares the approaches adopted by Saudi Arabia and the UK. By employing a mixed methods analysis, including interviews with SME owners and experts, the research highlights challenges and opportunities for improving cyber security governance and policy in both countries. The findings emphasize the need for tailored solutions due to the differing cultural and economic contexts between Saudi Arabia and the UK. Specifically, the study delves into the awareness and implementation of cyber security measures, focusing on SMEs in Saudi Arabia and their adherence to the Essential Cyber Security Controls (ECC-1:2018) guidelines. Furthermore, it examines the existing cyber security awareness practices and compliance in the UK, while also comparing official guidance documents aimed at supporting SMEs in achieving better cyber security practices. Based on the analysis, greater engagement with these documents is recommended in both countries to foster awareness, confidence, and compliance among SMEs, ultimately enhancing their cyber security posture. This paper offers a comparative research study on governance and policy between Saudi Arabia and the UK, presenting a set of recommendations to strengthen cyber security awareness and education, fortify regulatory frameworks, and foster public–private partnerships to combat cyber security threats in the Industry 5.0 landscape.
IET Generation, Transmission & Distribution, 2023
Detection of cyber-threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is ... more Detection of cyber-threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min-max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H 3 O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H 3 O-PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1-score to 99.2% increased, when compared to the standard techniques.
Mathematics, 2023
SQL injection attacks are one of the most common types of attacks on Web applications. These atta... more SQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an application’s database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then, it was compared with several other machine learning models, including ANN, CNN, decision tree, naive Bayes, SVM, random forest, and logistic regression models. The experimental results showed that the proposed approach achieved an accuracy of 94% and an F1-score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison to the other models covered in the study.
Sustainability, 2023
Recently, the concept of e-commerce product review evaluation has become a research topic of sign... more Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer’s opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets’ corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset.
Computer Systems Science and Engineering, 2023
Rapid technological advancement has enabled modern healthcare systems to provide more sophisticat... more Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospital management systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attributebased encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively.
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Books by Daniyal Alghazzawi
On January 03, 2002, “the FBI’s NIPC (National Infrastructure Protection Center) had strongly recommended that all users should immediately disable windows’ Universal Plug and Play support”. Then, GRC (Gibson Research Corporation) released a small tool to disable this service. "Universal Plug and Play" is one of the Windows’ services that opens a backdoor in your computer. There are more than 100 services that come with Microsoft Windows 7, and these services are under the Administrator Tools, so most people do not like to play with them. Therefore, GRC released a tool just to disable this specific service. Number of companies built number of tools to help you configure these services without the need to do it by yourself because they want to prevent users from playing with this technical complex issue. In my opinion, this technical issue is complex because they made it to be complex for us.
Therefore, I decided to take "Microsoft Windows Services" as an example in my book. I moved all the content from the tool developed by Microsoft to a website. Then, I applied number of steps, called the GOALAPE Model, on the website in order to get a comprehensible website. At the end, a survey has been conducted on a group of college students to measure their understanding of the original tool developed by Microsoft and my new website."
Papers by Daniyal Alghazzawi
Open AccessArticle
Enhancing Cyber Security Governance and Policy for SMEs in Industry 5.0: A Comparative Study between Saudi Arabia and the United Kingdom
by Nisha Rawindaran 1,Liqaa Nawaf 1,*ORCID,Suaad Alarifi 2,Daniyal Alghazzawi 2ORCID,Fiona Carroll 1ORCID,Iyad Katib 2ORCID andChaminda Hewage 1ORCID
1
Cardiff School of Technology, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
2
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Digital 2023, 3(3), 200-231; https://doi.org/10.3390/digital3030014
Received: 13 June 2023 / Revised: 19 July 2023 / Accepted: 31 July 2023 / Published: 14 August 2023
Download Browse Figures Review Reports Versions Notes
Abstract
The emergence of Industry 5.0 has revolutionized technology by integrating physical systems with digital networks. These advancements have also led to an increase in cyber threats, posing significant risks, particularly for small and medium-sized enterprises (SMEs). This research investigates the resistance of SMEs in Saudi Arabia and the United Kingdom (UK) to cyber security measures within the context of Industry 5.0, with a specific focus on governance and policy. It explores the cultural and economic factors contributing to this resistance, such as limited awareness of cyber security risks, financial constraints, and competing business priorities. Additionally, the study examines the role of government policies and regulations in promoting cyber security practices among SMEs and compares the approaches adopted by Saudi Arabia and the UK. By employing a mixed methods analysis, including interviews with SME owners and experts, the research highlights challenges and opportunities for improving cyber security governance and policy in both countries. The findings emphasize the need for tailored solutions due to the differing cultural and economic contexts between Saudi Arabia and the UK. Specifically, the study delves into the awareness and implementation of cyber security measures, focusing on SMEs in Saudi Arabia and their adherence to the Essential Cyber Security Controls (ECC-1:2018) guidelines. Furthermore, it examines the existing cyber security awareness practices and compliance in the UK, while also comparing official guidance documents aimed at supporting SMEs in achieving better cyber security practices. Based on the analysis, greater engagement with these documents is recommended in both countries to foster awareness, confidence, and compliance among SMEs, ultimately enhancing their cyber security posture. This paper offers a comparative research study on governance and policy between Saudi Arabia and the UK, presenting a set of recommendations to strengthen cyber security awareness and education, fortify regulatory frameworks, and foster public–private partnerships to combat cyber security threats in the Industry 5.0 landscape.
On January 03, 2002, “the FBI’s NIPC (National Infrastructure Protection Center) had strongly recommended that all users should immediately disable windows’ Universal Plug and Play support”. Then, GRC (Gibson Research Corporation) released a small tool to disable this service. "Universal Plug and Play" is one of the Windows’ services that opens a backdoor in your computer. There are more than 100 services that come with Microsoft Windows 7, and these services are under the Administrator Tools, so most people do not like to play with them. Therefore, GRC released a tool just to disable this specific service. Number of companies built number of tools to help you configure these services without the need to do it by yourself because they want to prevent users from playing with this technical complex issue. In my opinion, this technical issue is complex because they made it to be complex for us.
Therefore, I decided to take "Microsoft Windows Services" as an example in my book. I moved all the content from the tool developed by Microsoft to a website. Then, I applied number of steps, called the GOALAPE Model, on the website in order to get a comprehensible website. At the end, a survey has been conducted on a group of college students to measure their understanding of the original tool developed by Microsoft and my new website."
Open AccessArticle
Enhancing Cyber Security Governance and Policy for SMEs in Industry 5.0: A Comparative Study between Saudi Arabia and the United Kingdom
by Nisha Rawindaran 1,Liqaa Nawaf 1,*ORCID,Suaad Alarifi 2,Daniyal Alghazzawi 2ORCID,Fiona Carroll 1ORCID,Iyad Katib 2ORCID andChaminda Hewage 1ORCID
1
Cardiff School of Technology, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
2
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
*
Author to whom correspondence should be addressed.
Digital 2023, 3(3), 200-231; https://doi.org/10.3390/digital3030014
Received: 13 June 2023 / Revised: 19 July 2023 / Accepted: 31 July 2023 / Published: 14 August 2023
Download Browse Figures Review Reports Versions Notes
Abstract
The emergence of Industry 5.0 has revolutionized technology by integrating physical systems with digital networks. These advancements have also led to an increase in cyber threats, posing significant risks, particularly for small and medium-sized enterprises (SMEs). This research investigates the resistance of SMEs in Saudi Arabia and the United Kingdom (UK) to cyber security measures within the context of Industry 5.0, with a specific focus on governance and policy. It explores the cultural and economic factors contributing to this resistance, such as limited awareness of cyber security risks, financial constraints, and competing business priorities. Additionally, the study examines the role of government policies and regulations in promoting cyber security practices among SMEs and compares the approaches adopted by Saudi Arabia and the UK. By employing a mixed methods analysis, including interviews with SME owners and experts, the research highlights challenges and opportunities for improving cyber security governance and policy in both countries. The findings emphasize the need for tailored solutions due to the differing cultural and economic contexts between Saudi Arabia and the UK. Specifically, the study delves into the awareness and implementation of cyber security measures, focusing on SMEs in Saudi Arabia and their adherence to the Essential Cyber Security Controls (ECC-1:2018) guidelines. Furthermore, it examines the existing cyber security awareness practices and compliance in the UK, while also comparing official guidance documents aimed at supporting SMEs in achieving better cyber security practices. Based on the analysis, greater engagement with these documents is recommended in both countries to foster awareness, confidence, and compliance among SMEs, ultimately enhancing their cyber security posture. This paper offers a comparative research study on governance and policy between Saudi Arabia and the UK, presenting a set of recommendations to strengthen cyber security awareness and education, fortify regulatory frameworks, and foster public–private partnerships to combat cyber security threats in the Industry 5.0 landscape.
scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research