SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Detecting abuse of cloud and public legitimate services as command and control infrastructure using machine learning(Cardiff University, 2024) Al lelah, Turki; Theodorakopoulos, GeorgeThe widespread adoption of Cloud and Public Legitimate Services (CPLS) has inadvertently created new opportunities for cybercriminals to establish hidden and robust command-and-control (C&C) communication infrastructure. This abuse represents a major cybersecurity risk, as it allows malicious traffic to seamlessly disguise itself within normal network activities. Traditional detection systems are proving inadequate in accurately identifying such abuses. Therefore, this thesis is motivated by emphasizing the urgent need for more advanced detection techniques that are capable of identifying the C&C activity hidden within legitimate CPLS traffic. To assess the extent of the cyber threat of abusing CPLS, this thesis presents an ex- tensive Systematic Literature Review (SLR) encompassing academic and industry lit- erature. The review provides a comprehensive categorization of the attack techniques utilized to abuse CPLS as C&C infrastructure. The open problems uncovered through the SLR motivate this thesis to propose a novel Detection System (DS) capable of identifying malware that abuse CPLS as C&C communication channels. Furthermore, to evaluate our system robustness against attempts to evade detection, this thesis intro- duces the Replace Misclassified Parameter (RMCP) adversarial attack. The proposed detection system leverages Artificial Intelligence (AI) techniques, combining static and dynamic malware analysis methods to accurately identify CPLS abuse. The effective- ness of the proposed system is validated through extensive experiments, demonstrating its ability to detect novel and sophisticated attacks that evade traditional security measures. The outcomes of this thesis have significant implications for enhancing the security of cloud environments, contributing valuable knowledge and practical solutions to the field of cloud security.26 0Item Restricted Enhancing the quality and standard of research in Saudi Arabian universities: Selection and use of research methods at doctoral level: An investigative study on the use of research into statistical methods(University of glasgow, 2024) Abohiamid, Manal; McMahon, Margeryhis study investigated the selection and use of research methodologies at doctoral level, with particular emphasis on the use of statistical practices in research and with a focus on the Saudi Arabian context. There are misapplications in certain studies when analysing the statistical data, and some of these inaccuracies come from using improper management and suitable statistical methods at the analysis stage, contributing to misleading research conclusions. A key question was ‘How do academics and PhD students from Education departments in a selected university in Saudi Arabia and a university in Scotland/United Kingdom, from different educational backgrounds, view their readiness, selection, and utilization of statistical methods in PhD research?’. The accurate use of Statistics is critical in academic research, Statistics provide a methodical and objective approach to data analysis and interpretation, allowing researchers to make meaningful conclusions and uncover noteworthy patterns. Thus, a study acquires credibility and assures the validity of its findings by using accurate statistics, allowing policymakers and stakeholders to make educated decisions and perform targeted adjustments that enhance every aspect of society. This study examined PhD students' perceptions of their preparedness for statistical analysis, as well as their statistical and mathematical skills. Currently in Saudi Arabia, a programme of development: Vision 2030, is being implemented and so an aim of this study was to show why reforms are needed in Saudi Arabia's education system and why future university students should have sufficient Mathematical understanding to maintain the PhD researcher's basic knowledge base (Mathematics and Statistics). This was accomplished by sending a questionnaire to PhD students in SA and UK and conducting interviews with Statistics lecturers for postgraduate students in Saudi Arabia. The study found there is data analysis problems, such as inaccurate statistical technique application, a lack of pre-existing Mathematical expertise, wrong data processing, and incorrect result analysis. To increase the accuracy of statistical methods employed in PhD research, the study recommends that qualified statisticians, Statistics centres, and quality Mathematics and Statistics material be developed in the KSA. Furthermore, the research showed the significance of developing educational cadres in Statistics, developing the literacy pathway in schools in Mathematics, and making advanced Statistics courses necessary for postgraduate students in order to improve the quality and credibility of research undertaken, and the significance of students having a mathematical foundation in the Kingdom of Saudi Arabia26 0Item Restricted Measuring The Quality of Wikipedia Articles Among Different Topics(Saudi Digital Library, 2023-11-23) Aljohani, Thamer; Niesen, JitseWikipedia, a globally famous online encyclopedia, offers millions of articles across diverse topics. Its open editing policy, allowing contributions from volunteers, has made it a valuable resource. However, its reliability has been questionable, particularly in academic circles. To enhance the understanding of Wikipedia’s quality, and due to the difficulty of the assessment of quality in Wikipedia’s approach, this study presents an innovative approach to evaluate article quality. This study aims to create a quantifiable simple model based on measurable attributes, such as the length of articles, the number of references, and the number of edits. This model facilitates the calculation of article quality and subsequent assignment of quality classifications. As a result, the model proposed in this study shows an approximate accuracy equal to a random forest model which is considered a complex model. Furthermore, the research explores variations in article quality across various topics, shedding light on topics where high-quality content is prevalent and areas that require improvement. Data was collected from the Wikipedia API, and based on these measurable features, quality assessments were made. The findings indicate that Astronomy topics have a higher level of quality, while Language topics have a lower proportion of high-quality topics. These findings suggest that the attributes used to measure quality in this study are sufficient and efficient for assessing article quality on Wikipedia. Moreover, the study highlights the articles that need the experts to focus their efforts on improving articles related to topics such as Language, Business, or Mathematics to enhance the overall quality of content in these topics.10 0