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In the dynamic field of cybersecurity within intelligent vehicle systems, the sophistication of threats necessitates continual advancements in security technologies. Traditional Network Intrusion Detection Systems (NIDS) often fall short in detecting emerging and sophisticated intrusion methods, primarily due to their reliance on static datasets that fail to capture the nuanced dynamics and complexity of modern network intrusions. This study presents a sophisticated simulation for NIDS tailored to intelligent vehicle environments, utilizing the extensive capabilities of Scapy—a robust network manipulation tool—to generate a highly accurate dataset of network traffic reflective of real-world scenarios. We created a diverse dataset involving 100,000 network flows, covering a wide array of benign, malicious, and anomalous traffic patterns, to thoroughly evaluate the detection capabilities of our proposed system. This dataset was analyzed using a deep learning framework employing a Convolutional Neural Network (CNN), which demonstrated outstanding performance metrics: an accuracy of 99.08%, precision of 98.96%, recall of 99.11%, and an F1 score of 99.03%. These metrics showcase the system's enhanced capability to precisely classify various network flows, emphasizing the importance of realistic synthetic data in boosting the training and accuracy of NIDS in intelligent vehicles. The results of this research are significant, marking a step forward towards more flexible and preemptive security measures for intelligent vehicles, and effectively narrowing the gap between simulation-based testing and real-world network environments.

Mobile learning application now is considered a powerful application for learning and was adopted in universities in the period of Covid-19. After Covid-19 pandemic, university students have been allowed to use mobile learning systems, it is needed to ensure students’ intention to continuously use mobile learning for their learning activities or not. Thus, the purpose of this paper is to understand the main determinants that encourage the continuous use of mobile learning. To achieve that, we used the UTAUT-2 model to predict the main determinants of mobile learning acceptance. In our study, a quantitative technique was employed to collect the data. A hybrid approach SEM-ANN was applied to validate the research model. The findings indicated that performance expectancy and effort expectancy had a strong effect on students' attitudes towards mobile learning platforms. In addition, the results showed that performance expectancy and effort expectancy have a significant impact on students' continuous intention to use mobile learning platforms after Covid-19. In addition, hedonic motivation and habit had a positive effect on both students' attitudes and continuous intention to use mobile learning platforms. Moreover, Social influence factor and facilitating conditions had a significant effect on students' continuous intention to use mobile learning platforms after Covid-19.

Advanced green technology developments have induced key innovation approaches in the manufacturing industry to boost sustainable development (SD) in the present market. Despite the industrial need for green innovation and its associated benefits to regulate and optimise their operations and environmental performance, respectively, green innovation implementation and adoption remain inadequate. Due to the key significance of green innovation for firms operating in emerging markets, this research endeavors to investigate the green innovation influence on the relationship between Environmental Management System adoption and impact on Environmental Performance. To bridge this gap, The current work aimed structuring and validating a study model via the integration of Resource-Based View (RBV) and Institutional theories with the Technology-Organisation-Environment (TOE) framework to persuade companies towards green innovation implementation. Survey questionnaires were disseminated to 183 employees in a manufacturing company to collect the study data, which were then assessed with partial least squares-structural equation modelling (PLS-SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). Resultantly, the model's integrated constructs of perceived benefits, top management support, coercive pressure, normative pressure, and mimetic pressure predicted green management accounting practices. Green management accounting practices directly and significantly impacted green environmental performance, while green innovation significantly and negatively moderated the ‘green management accounting practice-green environmental performance’ link. Thus, the integrated model provides decision-makers with clear implications of green practice and innovative technology adoption for optimal environmental performance. The outcomes derived from literature reviews on advanced green technologies implied a notable ‘green management accounting practice-environmental performance’ relationship within emerging countries.

Understanding the factors that influence student performance is crucial for improving educational outcomes. Thus, this study aims to examine the impact of socio-economic and psychological factors on student performance, less is known about how students' personal attitudes and behaviors across different departments and activities correlate with their academic success. This study employs exploratory data analysis (EDA) to identify trends and relationships within the dataset. Machine learning techniques, such as K-means clustering and Long Short-Term Memory (LSTM) networks, are utilized to model and predict student performance based on their reported behaviors and preferences. The dataset is reduced using Principal Component Analysis (PCA) to enhance the clustering process. The findings suggest significant variations in academic performance based on departmental affiliation, gender, and engagement in certification courses. The LSTM model achieved an accuracy of 91% on the test set, demonstrating substantial predictive capability. However, the classification report reveals that while the model was highly effective in identifying the majority class (label 1), achieving a precision of 91% and a recall of 100%, it failed to correctly predict any instances of the minority class (label 0). The insights from this study could help educators tailor interventions to address the specific needs of students based on their behaviors and departmental affiliations, leading to more personalized education strategies and potentially improving academic outcomes. Doi: 10.28991/ESJ-2024-08-04-05 Full Text: PDF

Abstract The study was structured to investigate the impact of technological factors on the effectiveness of enterprise resource planning (ERP) in Jordanian industrial firms, using a descriptive-analytical approach. This study also examines company size as a moderator variable, which has been measured using the logarithm of total assets extracted from each firm’s annual report. The study’s target population consisted of all Jordanian industrial firms listed on the Amman Stock Exchange, totaling 55 firms. Managers participated as respondents, with 220 questionnaires distributed electronically, averaging four questionnaires per company. After data collection, 92 valid questionnaires were used for statistical analysis. The findings indicate a significant and positive relationship between technological factors (such as ease of use, perceived usefulness, and digital proficiency) and ERP system effectiveness in Jordanian industrial firms. However, the study revealed that trust, as one of the technological factors, did not significantly impact ERP system efficiency. Additionally, it was found that the moderating role of company size moderates positively the relationship between two technological factors (i.e., perceived usefulness and digital proficiency), and ERP system efficiency. The research suggests that industrial firms in Jordan should take into account technological factors, including factors like ease of use, perceived usefulness, and digital proficiency, to remain current with advancements that can improve the efficiency of ERP systems. This is important due to the positive influence it can have on the operational effectiveness of Jordanian industrial companies, aiding them in their planning and decision-making processes.

The main aim of this research was to explore how the digital proficiency of internal auditors impact the relationship between adopting cloud-based accounting and the digital efficiency of internal auditors in Jordanian-listed industrial firms. 98 questionnaires, gathered from internal audit managers in these companies, underwent analysis using Statistical Package for Social Sciences (SPSS) using descriptive-analytical methods. Results indicated that adopting cloud-based accounting significantly influences the effectiveness of internal auditors in these Jordanian-listed industrial companies. Moreover, the study found that the digital proficiency of internal auditors positively affects how much cloud-based accounting adoption impacts their effectiveness. These findings hold importance for regulators and decision-makers, offering insights to shape potential new regulations aimed at enhancing the use of cloud-based accounting in internal audit departments within Jordan's industrial sector. This could ultimately help internal auditors in overseeing public funds. Given the extensive impact of cloud technology on modern business, it's crucial to understand how these innovations can improve the precision and benefits of internal audit functions. Exploring the factors that drive the implementation of cloud technologies in internal audit is also essential. Notably, this study represents the initial attempt to examine existing research on the role of cloud-based accounting in enhancing internal audit effectiveness concerning the digital proficiency of auditors within Jordan's industrial sector.

Virtual Power Plants (VPPs) have emerged as dynamic solutions, harnessing the distributed resources such as solar panel and batteries. However, the complicated interconnectedness of VPPs exposes them to unique cybersecurity vulnerabilities, distinguishing them from traditional smart grids. This systematic literature review primarily focuses on the cybersecurity challenges VPPs face, such as data breaches and data flow manipulation. The study explores these unique risks based on proposed solutions from the existing literature like federated learning, intrusion detection systems, blockchain integration, and machine learning by analyzing their strengths, weaknesses, and applicability to VPP cybersecurity. Additionally, regulatory barriers and opportunities like cost-savings are discussed, with a focus on ensuring the secure future of VPPs through machine and deep learning and strong security policies. This paper contributes significantly to the field by identifying VPP cybersecurity risks, evaluating potential solutions, and proposing future research directions.

Several cloud-based applications are used for digital healthcare data allocation, but such an approach relies on third-party software such as the cloud. Existing models mainly utilize blockchain as a data storage tool rather than a security platform. Biomedical and monitoring devices generate massive amounts of data, and the existing approach overloads the blockchain with IoT data. This research proposes blockchain as a unique method for securing patient-related data access and integrating homomorphic encryption with an end-to-end privacy-protecting system. In this research, we propose a blockchain-based architecture for identifying security threats in personal medical devices to address the existing issues related to healthcare devices. The proposed framework uses certificate authority to assign an access control token in order to access a particular session. A certificate authority is the nodes based on the reputation within the blockchain network elected through consensus protocol. Proposed framework uses dual certificate authorities, which leads to more reliability and security if one certificate authority is down. Moreover, the existing algorithm overburden the medical devices which are resource constraint such as power oriented and such approaches leads to storage and communication cost overhead. By minimizing latency, security, and data ownership, the proposed framework outperforms the existing centralized system, by comparing the framework and evaluating its performance with the benchmark models.

Promoting sustainability development in education is a global endeavor, aiming to foster the sharing of experiences and knowledge on sustainability development. To achieve that, educational institutions worldwide have increasingly embraced educational technology and integrated online learning components into their instructional methods. This research focuses on the pivotal role of students as influential catalysts for advancing sustainable development within higher education. Specifically, it investigates the extent of students' familiarity with sustainable development initiatives within higher education institutions in the UAE. To achieve this objective, the study introduces the Technology-Integration Framework for Education Sustainable Development (TIFESD), which serves as an evaluative tool for appraising students' awareness of technology-driven elements woven into the broader context of Education for Sustainable Development (ESD) within their respective universities. The research employs a quantitative methodology, encompassing the collection of 513 survey responses from students across nine universities in the UAE. This data analysis explores the potential relationship between the integration of technology and students' cognizance of factors that bolster sustainable development. The study's outcomes underscore students' profound awareness of a spectrum of technology-driven elements, including Green Campus initiatives, Smart Education strategies, Smart Campus facilities, and the influence of curriculum and course offerings—all of which collectively contribute to the advancement of sustainable development practices within higher education institutions.

The aim of this study was to examine how the implications of the Covid-19 pandemic moderate the impact of Artificial Intelligence (AI) on the effective application of Cyber Governance (CG) in Islamic banks. A total of 93 questionnaires from branch heads of Islamic banks were used in this study, and the data were analyzed using the Statistical Package for Social Sciences (SPSS) through descriptive-analytical methods. The findings indicated that AI has a significant influence on the effective application of CG in Islamic banks. The study also revealed that the Covid-19 Pandemic positively moderates the influence of AI on the effective application of CG in Islamic banks. The results of this study have implications for regulators and decision-makers in proposing new legislation to effectively apply CG in the Islamic banking sector, which can help protect public funds and limit cyber-attacks. This study is the first to investigate the moderating effect of the Covid-19 pandemic on the influence of AI on the effective application of CG in Islamic banks.

This study examines the relationship between the types of Artificial Intelligence (AI) technology employed and monitoring tax payments. A thorough literature review is conducted to examine different AI technologies in the context of tax administration. These include machine learning algorithms (MLA), natural language processing (NLP) technology, robotic process automation (RPA), explainable artificial intelligence (XAI), and advanced data analytics techniques (DAT). A variety of technologies, such as big data analytics, task automation, task automation, unstructured data analysis, and predictive modeling, are available to improve tax payment monitoring procedures. Recommendations for further study to expand our knowledge and use of AI in tax payment monitoring are included, along with the consequences of AI adoption for tax authorities, policymakers, and practitioners.

A Distributed Denial of Service (DDoS) attack occurs when an attacker tries to disrupt a network, service or website by flooding huge numbers of packets on the internet traffic. Detecting DDoS attacks serves the goal of spotting and addressing them promptly to reduce their effects on the network, system or service being targeted. Detecting Distributed Denial of Service (DDoS) attacks is crucial, for people, companies and network managers. The detection of DDoS attacks has ranging uses in industries such as network security safeguarding websites, managing cloud services ensuring the security of online systems and services. Detecting DDoS attacks is essential for safeguarding infrastructure upholding service availability and guaranteeing the security of online systems and services. To achieve this objective, we proposed a framework to detect DDoS attacks including six steps. In step one, we start by gathering information, which includes network activity and system records, for operations as well as instances of DDoS attacks. Step two, we identify characteristics of the data collected such as patterns in network traffic, packet details, IP addresses, types of protocols used and more. Step three, we utilize algorithms for feature selection such as Salp Swarm Algorithm (SSA), Gray Wolf Algorithm (GWA), Particle Swarm Algorithm (PSO) to pinpoint the features that can distinguish between normal activities and DDoS attack patterns. After that in step four, we divide the processed dataset into sections for training and testing purposes to develop and assess the machine learning models such as SVM (support vector machine), and KNN (K-nearest neighbor). Step five we develop a classification model using machine learning techniques like decision trees, forests, support vector machines (SVM) logistic regression models or neural networks. Finally, we assess the effectiveness of models through metrics such as accuracy rates, precision levels, recall rates, and F1 scores. The results show that the proposed models achieve high results (99.9%). In summary detecting DDoS attacks is crucial for protecting networks, systems and online services against disruptions.

In the ever-evolving landscape of supply chain management, the quest for efficiency has become paramount. This abstract explores a groundbreaking solution that combines the power of permissioned blockchain technology with innovative data reduction strategies to redefine how supply chains operate. Traditional supply chain systems often grapple with data overload, causing delays, inaccuracies, and operational inefficiencies. However, this abstract presents a promising approach that unleashes efficiency by harnessing the capabilities of a permissioned blockchain. Through data reduction techniques tailored to the needs of supply chain management, this approach streamlines the flow of information while maintaining security and trust among participants. This paper seeks into the technical foundations of permissioned blockchains, highlighting their suitability for supply applications where confidentiality and controlled access are imperative. Furthermore, it examines various data reduction methodologies, emphasizing their role in minimizing redundant data, optimizing communication, and enabling real-time decision-making. The impact of this innovative approach on supply chain stakeholders is profound. It reduces data related bottlenecks, enhances transparencies, and fosters collaboration among participants. Additionally, it provides a scalable framework adaptable to diverse supply chain ecosystems. As supply chain efficiency becomes increasingly important in our interconnected world, this permissioned blockchain-driven data reduction strategy offers a compelling vision for the future. It promises to unlock a new era of streamlined operations, cost savings, and improved customer satisfaction, ultimately shaping the next generation of supply chain management.

With flexible learning, students are actively engaged in their own education and are held to high standards of performance. Online academic courses make it easier for students to receive personalized education because they provide students with more flexibility to concentrate on what is most important to them and give them greater control over their own education. This study’s objective was to investigate whether there is a correlation between how well students succeed in online classes and the extent to which they make use of the schedule and the geographical and resource flexibility offered by such programmes. This article uses a developing approach for predicting and classifying the flexibility in online learning of students who are at risk of failing due to academic and demographic variables. The K-nearest neighbours (KNN) method, the random forest (RF) method, and the logistic regression method were used to categorise the students participating in flexible online learning. The information for the dataset came from Kaggle, and it was gathered for use in testing machine learning. The dataset had a total of 1,875 instances representing 11 different features. Also, accuracy, precision, sensitivity and f-score metrics were applied to evaluate the system. The results show that the RF algorithm has a high accuracy percentage of 85%. The empirical findings demonstrate that students formed distinct patterns of learning time, location and access to knowledge. This suggests that flexibility was used to a significant degree. Patterns in learning time and the availability of learning materials were shown to have a substantial relationship with the accomplishments of the students. Understanding flexibility use habits may help adapt lessons and boost collaboration among similar students.

Vehicular fog computing (VFC) is a developing concept that utilizes the ideas of fog computing to facilitate immediate communication and cooperative decision-making among vehicles. However, guaranteeing safe authentication in VFC presents notable difficulties as a result of characteristics such as dynamic network topology, extensive mobility, and limitations on resources. This paper introduces D-BlockAuth, an innovative authentication mechanism for 5G-assisted VFC that utilizes a dual blockchain approach. To reach the most vehicles, this link makes use of all the features of the fifth-generation base station (5G-BS). D-BlockAuth employs two blockchains: a permissioned blockchain to handle long-term identities and a consortium blockchain to enable streamlined and effective authentication at the fog layer. The D-BlockAuth concept incorporates advanced cryptographic techniques such as ring signatures and group signatures, which provide improved privacy and anonymity for vehicles within the network. The study provides a comprehensive description of the architecture of D-BlockAuth, examines its security characteristics, and assesses its performance using simulations. The results indicate that D-BlockAuth successfully performs both efficient and safe authentication in VFC, while also maintaining user privacy.

This research significantly contributes to comprehending the body of work surrounding green economics and sustainable economies by methodically reviewing and categorizing papers from the Web of Science (WoS) core collection. It highlights key authors, subjects, publishing sources, and nations relevant to green economic studies by meticulously analyzing 4,157 papers. Additionally, it constructs detailed visual maps based on referenced sources. By conducting a thorough bibliometric analysis, this study underscores the importance of using such tools to gauge research impact and productivity. Leveraging the WoS Core Collection, particularly the Science Citation Index Expanded and the Social Sciences Citation Index, was deliberate to ensure a comprehensive evaluation of green economic research. The data accentuates the changing landscape of this field, indicating its growing significance and the expanding academic discussions around sustainable economic practices. The research delves into emerging research directions that could inform considerations for a sustainable economy. Despite the marked increase in publications in recent years, signaling heightened interest in green economics and sustainable economy studies, this field remains in its infancy, with limited quantitative studies conducted. Consequently, many findings remain inconclusive, and numerous aspects in literature remain unexplored. Finally, this study outlines both practical and theoretical implications gleaned from its findings.

Consumers’ willingness to pay a price premium is pivotal for assessing brand value and competitive advantage. Yet, limited and scattered research has focused on how combining brand emotion, strength, and brand loyalty can influence consumers’ willingness to accept a price premium. The present study examines the role of brand attachment, brand strength, and brand loyalty in determining consumers’ willingness to pay a price premium and explores their interplay using a serial mediation model within a unified framework, specifically focusing on home appliance brands. Data from 323 valid questionnaires collected from Algerian households were analyzed using PLS-SEM. Results demonstrate that consumers’ willingness to pay a price premium is significantly and positively influenced by brand strength, brand attachment, and brand loyalty. Furthermore, the relationship between brand strength and consumers’ willingness to pay a price premium is mediated positively by brand attachment and brand loyalty. Grounded on various theories and addressing gaps captured in previous studies, this research is considered pioneering in this field. This study significantly advances our understanding of how brand emotional bonds, brand relationships, and brand strength interplay to influence consumers’ willingness to pay a premium. The findings highlight the importance for brand managers to sustain robust brands to stimulate consumers’ opening to pay extra, thereby achieving and maintaining long-term success in a competitive market.

AbstractThe study designed to identify the influence of artificial intelligence on the efficiency of Accounting Information Systems (AIS) in the Jordanian industial companies, where the descriptive analytical approach was used. The study population included all Jordanian industrial companies listed in Amman Stock Exchange, includes (55) industrial companies. The respondents includes the managers, where (275) questionnaires were distributed to the study respondents electronically, with an average of (5) questionnaires for each company, and retrieved 142 valid questionnaires for statistical analysis. The results presented that there is a significant and positive effect of artificial intelligence’s dimensions (i.e. expert systems, genetic algorithms, intelligent agents) on the efficiency of AIS in Jordanian industrial companies. But the resule revealed that neural network as an artificial intelligence’s dimensions have non-significant effect on the efficiency of AISs. The result also found that the effective application of cyber security moderates the relationship between artificial intelligence and the the efficiency of AIS positively. Given the importance of the industrial companies’ sector in the context of Jordan, the results are useful for these companies with regard to the issue of AIS efficiency and the role of artificial intelligence applications in this efficiency. The results also highlighted the importance of the effective application of cyber security in such a technological era. To the knowledge of the authors, this study is one of the first to address cybersecurity as a moderating variable on the relationship between artificial intelligence applications and AIS efficiency in industrial companies in Jordan, a developing country.

This study emphases on the top management empowerments to internal auditors, namely, general qualification, electronic qualification and independence, affecting quality of electronic internal audits in the Jordanian Listed Service Companies. This paper used 144 usable questionnaires from internal auditors in the Jordanian listed service companies. The gathered data were analysed utilizing ``Statistical Package for Social Sciences (SPSS)''. The results reveal that general qualification, electronic qualification and independence have a significant effect on the quality of electronic internal audits, as supported by the resource-based view. Due to the importance of the service companies’ sector in the context of Jordan, the results are helpful for the internal audit profession and decision makers in offering new legislation for the internal audit profession. Future research may consider other factors that may hinder the quality of electronic internal audits, such as audit task complexity or organizational culture.

An effective internal auditor can support an organization in achieving its goals and protect its assets and funds. However, to be effective, the internal auditors need to be empowered with relevant resources. This study aims at probing the influence of the empowerments (management support, external auditors' collaboration, independence, size of internal audit units, and extrinsic rewards) on the internal auditors' effectiveness, and to examin whether extrinsic rewards moderate the association between respective empowerments and the effectiveness of Jordanian public sector internal auditors'. The current work relied on 117 surveys collected from Jordan's internal audit and financial managers in the public sectors. According to the Resource-Based View (RBV), the findings show that management support, external auditors cooperation, independence, and extrinsic rewards all have a significant influence on the effectiveness of internal auditor. The of the Internal Audit Department (IAD) size was discovered to be insignificant. Also, the results have revealed partial support for the influence of the extrinsic reward as a moderator. Because of the prominence of the public sector in the Jordanian market, this current research expands on the idea of enabling internal auditor (IA) to accomplish their assigned objectives. The findings might help authorities develop new norms and legislation for the internal audit profession. These empowering characteristics may also improve internal auditors' capacity to execute their duty in saving public monies and limiting corrupt practices inside public sector organizations.

Despite tax being a fundamental method to redistribute wealth and achieve a sustainable economic and social system, tax agencies and institutions in most countries are struggling with low tax collections. This issue is often attributed to the level of compliance among taxpayers. To gain more insight into this problem, a study was conducted to examine how socio-economic determinants such as probability of detection, tax complexity, tax penalty, tax sanctions, tax ethics, tax justice, government spending, and tax services quality impact VAT compliance decisions. The study drew a random sample of 770 retail industry participants from Jordan, an Arabic country, for a self-administered survey. Smart-PLS structural equation modeling was used to analyze and estimate the compliance model. The results indicated that all proposed direct relationships were supported, and the interactions between tax knowledge and the socio-economic determinants on VAT compliance were found to be significant. The findings of this research can be useful for policymakers and institutions responsible for taxpayers' communities to understand the role of tax knowledge in VAT compliance in the retail industry. The study emphasizes the significance of instilling tax knowledge, social and moral values among VAT payers, establishing an equitable system, and launching awareness programs in Jordanian society. Additionally, it contributes to existing literature by confirming a practical compliance model rooted in the socio-economic theory of regulatory compliance. This model incorporates the moderating effect of tax knowledge within socio-economic aspects of VAT compliance. By understanding the importance of tax knowledge, policymakers and institutions can develop effective strategies to boost VAT funds and improve compliance in the retail industry. This can ultimately lead to increased government revenues without placing an undue economic burden on lower-income taxpayers.

The current study investigates how perceived risks and trust factors affect customers' intentions to use mobile payment applications that utilize near-field communication (NFC) technology. The author created a conceptual model that includes five elements: intention to use, perceived risk, process-based trust, characteristics-based trust, and institution-based trust. Data was gathered through an online survey of 469 participants and analyzed using partial least squares structural equation modeling techniques. The results indicate that only three of the four tested constructs significantly impact customers' decisions to use NFC mobile payments: perceived risk, process-based trust, and characteristics-based trust. Additionally, the study found that age and gender do not play a significant role in influencing the model constructs; therefore, the findings of current research highlight how perceived risk and trust have a pivotal role of in shaping customers' intention to use NFC as a mobile payment and how trust can substantially mitigate customers' perceived risk. Hence, this knowledge can aid in developing effective strategies for promoting mobile payment adoption among consumers.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users’ security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

Mobile learning has become increasingly important for higher education due to its numerous advantages and transformative potential. The aim of this study is to investigate how students perceive and utilize mobile learning (m-learning) services in universities. To achieve this objective, a conceptual model was developed, combining the TAM with additional new determinants, including perceived security, perceived trust, perceived risk, and service quality. The primary goal of this model is to assess the adoption of m-learning apps among users in university settings. To evaluate the proposed model, SEM was utilized to test the research model. The findings of the study highlight the critical roles of perceived security, perceived trust, and service quality in promoting the adoption of m-learning apps. Moreover, the results indicate that perceived risk negatively impacts both students’ trust and their attitudes towards using mobile learning services. The study reveals that the perceived trust, and service quality factors positively influence students’ attitudes towards adopting m-learning apps. These research findings hold significant implications for universities and academia, offering valuable insights to devise effective strategies for increasing the utilization of m- learning services among students. By gaining a deeper understanding of students’ perceptions and acceptance, universities can optimize their m-learning offerings to cater to students’ needs and preferences more effectively.

The investigation of users' satisfactions and intentions in using the services provided by commercial banks needs to be focused on internet banking, since this is the widely used banking service. This paper analyzed the satisfactions and behavioral intentions of Malaysian customers in using Internet Banking, applying the Information System Success Model (ISSM) by the integration of adoption and application technology (UTAUT) theory. Some criteria, which were taken into consideration, are as follows: perceived Risk (PR), facilitating Conditions (FC), Price (PV), Performance expectancy (PE), Information Quality (IQ), Service Quality (SEQ), and System Quality (SQ). These aspects are important to measure customers' satisfaction and behavior toward Internet and Online Banking. A sample of 362 valid responses, consisting of Malaysian customers who used E-Banking, was used for the purpose of data collection. The relationship between customers' satisfaction and factors influencing their contentment in using Online Banking was investigated. In order to evaluate this, a model called “Structural Equation Model (SEM)” was developed and used. Findings showed that most of the Online Banking users were satisfied with the system. Hence, it proves that Online Banking System was generally accepted in the Malaysian community. However, this current research is solely focused on the Malaysian community. Therefore, it may provide the necessary grounds for the study of internet banking accreditation, some useful information for the practitioners, policy makers, and the researchers to improve the system of online banking in Malaysia only. For a future study, a more well-structured research, which can accommodate the global need, would be required.

Multi-label Classification (MLC) is a very interesting and important domain that has attracted many researchers in the last two decades. Several single label classification algorithms that belong to different learning strategies have been adapted to handle the problem of MLC. Surprisingly, no Associative Classification (AC) algorithm has been adapted to handle MLC problem, where AC algorithms have shown a high predictive performance comparing with other learning strategies in single label classification. In this paper, a deep investigation regarding utilizing AC in MLC is presented. An evaluation of several AC algorithms on three multi-label datasets with respect to five discretization techniques reveals that utilizing AC algorithms in MLC is very promising, comparing with other algorithms from different learning strategies. [JJCIT 2021; 7(2.000): 166-179]

Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple distributed data sources. However, DDM faces several problems in terms of autonomy, privacy, performance and implementation. DDM requires homogeneity regarding environment, control, administration and the classification algorithm(s), and such that requirements are too strict and inflexible in many applications. In this paper, we propose the employment of a Multi-Agent System (MAS) to be combined with DDM (MAS-DDM). MAS is a mechanism for creating goal-oriented autonomous agents within shared environments with communication and coordination facilities. We shall show that MAS-DDM is both desirable and beneficial. In MAS-DDM, agents could communicate their beliefs (calculated classification) by covering private and non-sharable data, and other agents decide whether the use of such beliefs in classifying instances and adjusting their prior assumptions about each class of data. In MAS-DDM, we will develop and use a modified Naive Bayesian algorithm because (1) Naive Bayesian has been shown to be the most used algorithm to deal with uncertain data, and (2) to show that even if all agents in MAS-DDM use the same algorithm, MAS-DDM preforms better than DDM approaches with non-communicating processes. Point (2) provide an evidence that the exchange of information between agents helps in increasing the accuracy of the classification task significantly.

Wireless Sensor Networks (WSNs) have revolutionized the era of conventional computing into a digitized world, commonly known as “The Internet of Things”. WSN consists of tiny low-cost sensing devices, having computation, communication and sensing capabilities. These networks are always debatable for their limited resources and the most arguable and critical issue in WSNs is energy efficiency. Sensors utilize energy in broadcasting, routing, clustering, on-board calculations, localization, and maintenance, etc. However, primary domains of energy consumption at node level are three i.e. sensing by sensing-module, processing by microprocessor and communication by radio link. Extensive sensing, over-costs processing and frequent communication not only minimize the network life-time, but also affects the availability of these resources for other tasks. To increase life-time and provide an energy-efficient WSN, here we have proposed a new scheme called “A Content-based Adaptive and Dynamic Scheduling (CADS) using two ways communication model in WSNs”. CADS dynamically changes a node states during data aggregation and each node adapts a new state based on contents of the sensed data packets. Analyzer module at the Base-Station investigates contents of sensed data packets and regulates functions of a node by transmitting control messages in a backward direction. CADS minimizes energy consumption by reducing unnecessary network traffic and avoid redundant message-forwarding. Simulation results have been shown that it increases energy-efficiency in terms of network life-time by 9.65% in 100 nodes-network, 11.36% in 150 nodes-network and 0.94% in 300 nodes. The proposed scheme is also showing stability in terms of increasing cluster life by 87.5% for a network of 100 nodes, 94.73% for 150 nodes and 53.9% in 300 nodes.

For the development effective and successful mobile learning applications, it is important to understand the users' requirements in different stages of usage. In this paper, we developed a new model to study the effect of different factors on mobile learning applications development at the three main stages of usage (static stage, interaction stage and transaction stage). The results of this study showed that each stage of the three stages, static, interaction, and transaction has different requirements in terms of system compatibility, security, information quality, awareness, perceived functional benefit, self-efficacy, perceived image, perceived uncertainty, availability of resources, and perceived trust. In addition, the results demonstrated that the requirements and perceptions of users towards the adoption and use of mobile learning application in the three stages significantly differ. The novelty of this research will be an added value to the body of knowledge and its implications will be vital for researchers and designers who are developing mobile learning applications.

This study aimed to alleviate the gap between the literature regarding the Social Networking Applications (SNAs) use for active collaboration and engagement as sustainability in higher education and task-technology-fit (TTF) and compatibility on their consequence on students' satisfaction and their performance impact its sustainability used in higher education. Although researchers have examined (SNAs) usage within multiple situations, the roles of (TTF) and compatibility as mediating variables have not been investigated through TAM model and constructivism theory on measuring education sustainability. Overall 602 students and researchers took part in this study, which were selected from public university. Using the method of structural equation modeling (SEM), we surveyed to discover the perception of students toward the (SNAs). Based on the results, the (SNAs) use for collaboration and engagement as sustainability in higher education, and TTF and compatibility positively impacted the student's learning performance on measuring education sustainability, and they were found to be completely pleased with the perceived ease of use and perceived usefulness. In conclusion, the role of TTF and compatibility presents positive influences performances related to sustainability for education; and both factors mediates associations among collaboration and engagement as sustainability in higher education, students' satisfaction on (SNAs) usage and students' performance related to sustainability for education. Therefore, their impact should be encouraged in learning processes in higher education institutions.

Owing to the limited resources of sensor nodes, we propose an efficient hybrid routing scheme using a dynamic cluster-based static routing protocol (DCBSRP), leveraging the ad hoc on-demand distance vector (AODV) routing protocol and low-energy adaptive clustering hierarchy (LEACH) protocol. In the proposed scheme, the cluster head (CH) nodes are formed dynamically for a fixed interval, whereas static routing is applied in the designated clusters by utilizing the AODV routing protocol. The static routing condition of the proposed scheme limits all connected nodes of the cluster for a defined interval of time (T) to share their collected information through a specific CH node. Once the time (T) interval is completed, all ordinary nodes connected with the specific CH are released and they are free to advertise their CH candidateship within the network. Likewise, the node receiving the maximum number of route replies (RREPs) is selected as the next CH node in the vicinity of deployed sensor nodes. However, with the DCBSRP protocol, the recently selected CH node does not advertise its candidateship for five consecutive cycles and acts as an ordinary node. The simulation result shows significant improvement in the lifetime and participation of ordinary nodes in the network until the end-stage of the network. In the proposed scheme, the participation of ordinary nodes in the network is 95.9 %, which not only balances load between participating nodes but also improves the network lifetime in the presence of field-proven schemes. Moreover, the simulation results show an out-performance of rival schemes in terms of communication cost, end to end delay, throughput, packet lost ratio, and energy consumption.

Wireless sensor networks (WSNs) is an infrastructure free organization of various operational devices. Due to their overwhelming characteristics, these networks are used in different applications. For WSNs, it is necessary to collect real time and precise data as critical decisions are based on these readings in different application scenarios. In WSNs, authentication of the operational devices is one the challenge issue to the research community as these networks are dynamic and self-organizing in nature. Moreover, due to the constraint oriented nature of these devices a generalized light-weight authentication scheme is needed to be developed. In this paper, a light-weight anonymous authentication techniques is presented to resolve the black-hole attack issue associated with WSNs. In this scheme, Medium Access Control (Mac) address is used to register every node in WSNs with its nearest cluster head (CH) or base station module(s). The registration process is performed in an off-line phase to ensure authenticity of both legitimate nodes and base stations in an operational network. The proposed technique resolves the black-hole attack issue as an intruder node needs to be registered with both gateway and neighbouring nodes which is not possible. Moreover, a hybrid data encryption scheme, elliptic curve integrated encryption standard (ECIES) and elliptic curve deffi-hellman problem (ECDDHP), is used to improve authenticity, confidentiality and integrity of the collected data. Simulation results show the exceptional performance of the proposed scheme against field proven techniques in terms of minimum possible end-to-end delay & communication cost, maximum average packet delivery ratio and throughput in presence of malicious node(s).

Resource limited networks have various applications in our daily life. However, a challenging issue associated with these networks is a uniform load balancing strategy to prolong their lifespan. In literature, various schemes try to improve the scalability and reliability of the networks, but majority of these approaches assume homogeneous networks. Moreover, most of the technique uses distance, residual energy and hop count values to balance the energy consumption of participating nodes and prolong the network lifetime. Therefore, an energy efficient load balancing scheme for heterogeneous wireless sensor networks (WSNs) need to be developed. In this article, an energy gauge node (EGN) based communication infrastructure is presented to develop a uniform load balancing strategy for resource-limited networks. EGN measures the residual energy of the participating nodes i.e., Ci ∈ Network. Moreover, EGN nodes advertise hop selection information in the network which is used by ordinary nodes to update their routing tables. Likewise, ordinary nodes use this information to uni-cast its collected data to the destination. EGN nodes work on built-in configuration to categorize their neighboring nodes such as powerful, normal and critical energy categories. EGN uses the strength of packet reply (SPR) and round trip time (RTT) values to measure the neighboring node's residual energy (Er) and those node(s) which have a maximum Er values are advertised as reliable paths for communication. Furthermore, EGN transmits a route request (RREQ) in the network and receives route reply (RREP) from every node reside in its closed proximity which is used to compute the Er energy values of the neighboring node(s). If Er value of a neighboring node is less than the defined category threshold value then this node is advertised as non-available for communication as a relaying node. The simulation results show that our proposed scheme surpasses the existing schemes in terms of lifespan of individual nodes, throughput, packet loss ratio (PLR), latency, communication costs and computation costs, etc,. Moreover, our proposed scheme prolongs the lifespan of WSNs and as well as an individual node against exiting schemes in the operational environment.

Mobile learning applications have been growing in demand and popularity and have become a common phenomenon in modern educational systems, especially with the implementation of mobile learning projects. This study applies the Unified Theory of Acceptance and Use Technology (UTAUT) model to examine the effects of different factors that were identified from the literature on students' acceptance of mobile learning applications in higher education. The data was collected from a 697 university students responded to an online questionnaire. SEM method was used for data analysis. The results showed that perceived information quality, perceived compatibility, perceived trust, perceived awareness, and availability of resources, self-efficacy, and perceived security are the main motivators of students' acceptance of mobile learning system, and consequently success the implementation of mobile learning projects. Results from this study provide the necessary information as to how higher education institutions can enhance students' acceptance of mobile learning system in order to support the usage of mobile technologies in learning and teaching process. These results offer important implications for mobile learning acceptance and usage.

This work aims to investigate the main determinants that could play an important role in increasing the usage and acceptance of e-learning systems among Saudi students. The study employed the Unified Theory of Acceptance and Use of Technology (e-UTAUT) model and introduced new constructs to study the acceptance of e-learning systems in the Saudi Arabian context. The data were collected using a questionnaire survey from a total of 507 undergraduate and postgraduate students at King Faisal University (KFU). The research model was tested using the structural equation modeling technique (SEM). Based on the results, the factors of course design, course content support, course assessment and instructor characteristics were shown to have a significant effect on the actual use of e-learning systems. However, the influence of social influence on actual use was found to be statistically insignificant. Additionally, the course design, course content support, course assessment and instructor characteristics factors were found to have a positive effect on the performance expectancy of e-learning systems. The research findings serve as an important reference for university managers and researchers on students’ priorities when improving course design, course content and course assessment, thereby increasing the usage and acceptance of e-learning systems among students.

The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and other concerned components. In the recent decade, it has been noted that the demand for this emerging technology is gradually increased with cost-effective results. Although this technology offers extraordinary results, but at the same time, it also offers multifarious security perils that need to be handled effectively to preserve the trust among all engaged stakeholders. For this, the literature proposes several authentications and data preservation schemes, but somehow they fail to tackle this issue with effectual results. Keeping in view, these constraints, in this paper, we proposed a lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices. With decentralized authentication, we have depreciated the validation latency among pairing devices followed by improved communication statistics. Moreover, the experimental results were compared with the benchmark models to acknowledge the significance of our model. During the evaluation phase, the proposed model reveals incredible advancement in terms of comparative parameters in comparison with benchmark models.

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

This study presents three frameworks for mobile learning system based on quality factors derived from the updated DeLone and McLean information system success model. This study used the questionnaire as a quantitative method to explore quality factors for mobile learning system based on perspectives of 392 students. This study opens future work for using the identified quality factors as guidelines for researchers and designers to design and develop mobile learning applications.

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.