1,720,974 research outputs found
Towards ensuring scalability, interoperability and efficient access control in a triple-domain grid-based environment
Full text linkPhilosophiae Doctor - PhDThe high rate of grid computing adoption, both in academe and industry, has posed
challenges regarding efficient access control, interoperability and scalability. Although several methods have been proposed to address these grid computing challenges, none has proven to be completely efficient and dependable. To tackle these challenges, a novel access control architecture framework, a triple-domain grid-based environment, modelled on role based access control, was developed. The architecture’s framework assumes three domains, each domain with an independent Local Security Monitoring Unit and a Central Security Monitoring Unit that monitors security for the entire grid.The architecture was evaluated and implemented using the G3S, grid security services simulator, meta-query language as “cross-domain” queries and Java Runtime Environment 1.7.0.5 for implementing the workflows that define the model’s task. The simulation results show that the developed architecture is reliable and efficient if measured against the observed parameters and entities. This proposed framework for access control also proved to be interoperable and scalable within the parameters tested
CyberProtector: Identifying Compromised URLs in Electronic Mails with Bayesian Classification
No full textClassification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques
No full textBackground: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed
Digital Twin Technology: A Review of Its Applications and Prominent Challenges
Full text linkDigital twin is a virtual representation of physical product that is used as benchmark to evaluate, diagnose, optimize and supervise operational performance of products before venturing into mass full production in accordance with global standard. Digital twin merges virtual and physical objects together via sensors and IoT to transmit data and keep traces of objects interactivity within present environments. In virtual model environment, digital twin permits product troubleshooting and testing to minimize rate of failure and product defects during product manufacturing to enhance effectiveness and customers’ satisfaction. Digital twin is utilized throughout product life-cycle to simulate, optimize and predict product quality before final production is financed. Digital twin is beneficial to modern digital society because attitude of modern factory workers can be boosted to improve motivation to work. Digital twin has come to stay, future product suppliers may be required to put forward digital twin of their products beforehand for virtual lab testing before making order while suppliers that fail to comply may be left over. With emergence of digital twin, virtual testing can be conducted on proposed products before finding their ways into physical marketplaces. Business sector remains most beneficiaries of digital twin to predict present and future state of physical product via digital peer analysis. Today, digital twin application can support enterprises by improving product performances, decision making and customers’ satisfactions on logistic and operational workflow. However, in this survey of digital twin research, efforts have been made to review in detail about digital twin, its impact and benefits to modern society, its architecture; security challenges and how solutions are proffered. It is believed that ICT experts, manufacturers and industries will leverage on this research to improve QoS (Quality of Service) for new and future products to take full advantage of profits on investment returns via digital twin
Digital Education: Assessment of e-Learning and m-Learning Adoption in Tertiary Institutions in South Africa
No full textSecurity and privacy issues in e-health cloud-based system: A comprehensive content analysis
No full textThe recent advancement in Information and Communication Technology (ICT) has undoubtedly improved services in all sectors in the world. Specifically, Information Technology (IT) has led to a very vital innovation in health sector called electronic health (e-Health). In order to optimize full and excellent benefits of this innovation, its implementation in a cloud-based environment is important. However, with noticeable and numerous benefits inherent from e-Health in a cloud computing, its full utilization is still being hampered by challenges of security and privacy. In this paper, we focused on extensive review of current and existing literatures of various approaches and mechanisms being used to handle security and privacy related matters in e-Health. Strengths and weaknesses of some of these approaches were enunciated. The literature review was carried out after selecting over One Hundred and Ten (1 1 0) original articles and figured out several models adopted in their solutions. After comparing models used, we arrived at the reviewed articles. Reviewed articles were narrowed down to the current number because of similarity observed in the models adopted by some researchers. Also, we give an acceptable and standard definition of e-Health. Effort was made to classify cloud-based models. Security and privacy requirements as recommended by Health Insurance Portability and Accountability Act (HIPAA) were also discussed and provided. Remarks and recommendations were made regarding the review process and future directions on security and privacy of e-Health in cloud computing was also provided. Finally, authors propose a secured and dependable architecture for electronic health that could guarantee efficiency, reliability and regulated access framework to health information. The architecture, though is currently under implementation, will guarantee absolute security and privacy between healthcare providers and the patients. Keywords: E-Health, Security and privacy, Cloud, Vulnerability, Access contro
Comparative Analysis of Encryption Algorithms
Full text link the vulnerable nature of some sensitive and classified information such as health and bank related data has undoubtedly caused serious havoc to individuals who should enjoy the privacy and confidentiality of their information. In an attempt to guarantee absolute security of information from one source to another and also to prevent confidential information from being revealed to unauthorized people, encryption algorithms are being used to achieve this. Encryption algorithms are basically useful for securing and protecting data being transmitted from one end to another from any form of vulnerability. Over the years, researchers have adopted some of these algorithms to ensure privacy of information in banking, health and military. Some of these algorithms are varied in terms of efficiency, accuracy, reliability and response time whenever they are used for data protection. In an attempt to carry out a comparative assessment, we considered Rivest-Shamir-Adleman (RSA), Advanced Encryption Standard (AES) and Data Encryption Standard (DES) algorithms. Since there is skepticism on which of the algorithms is more reliable, dependable and functional when considering features that characterized their variation, this work therefore, attempts to do a comparative assessment of each of the encryption algorithms to ascertain the best using the stated metrics. The implementation was carried out with C#. The results obtained from the experimentation revealed that AES uses the lowest time for encryption while RSA consumes longest encryption time. Also, AES algorithm is considered the most efficient of all the three algorithms based on the metrics used for the evaluation. Few of the results obtained are presented in this paper
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