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 Facial Emotion Recognition via Label Distribution Learning and Customized Convolutional Layers(The University of Warwick, 2024-11) Almowallad, Abeer; Sanchez, VictorThis thesis attempts to investigate the task of recognizing human emotions from facial expressions in images, a topic that has been interest of to researchers in computer vision and machine learning. It addresses the challenge of deciphering a mixture of six basic emotions—happiness, sadness, anger, fear, surprise, and disgust—each presented with distinct intensities. This thesis introduces three Label Distribution Learning (LDL) frameworks to tackle this. Previous studies have dealt with this challenge by using LDL and focusing on optimizing a conditional probability function that attempts to reduce the relative entropy of the predicted distribution with respect to the target distribution, which leads to a lack of generality of the model. First, we propose a deep learning framework for LDL, utilizing convolutional neural network (CNN) features to broaden the model’s generalization capabilities. Named EDL-LBCNN, this framework integrates a Local Binary Convolutional (LBC) layer to refine the texture information extracted from CNNs, targeting a more precise emotion recognition. Secondly, we propose VCNN-ELDL framework, which employs an innovative Visibility Convolutional Layer (VCL). The VCL is engineered to maintain the advantages of traditional convolutional (Conv) layers for feature extraction, while also reducing the number of learnable parameters and enhancing the capture of crucial texture features from facial images. Furthermore, this research presents a novel Transformer architecture, the Visibility Convolutional Vision Transformer (VCLvT), incorporating Depth-Wise Visibility Convolutional Layers (DepthVCL) to bolster spatial feature extraction. This novel approach yields promising outcomes, particularly on limited datasets, showcasing its capacity to meet or exceed state-of-the-art performance across different dataset sizes. Through these advancements, the thesis significantly contributes to the advancement of facial emotion recognition, presenting robust, scalable models adept at interpreting the complex nuances of human emotions.17 0Item Restricted The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, ThomasThis research explores the implementation of IoT-based predictive maintenance within railway systems, focusing on the technologies, cost implications, reliability, safety, and barriers identified in the literature. The study systematically reviews 30 peer-reviewed journals to assess the current state of IoT applications in the railway sector. Critical IoT technologies such as sensors, wireless sensor systems, and edge processing are examined in their role in enhancing predictive maintenance practices. The research highlights significant long-term cost savings associated with IoT adoption, despite high initial implementation costs. Furthermore, the study evaluates how IoT technologies contribute to improved reliability and safety by enabling real-time monitoring and predictive analysis. However, several barriers to widespread adoption are identified, including technical integration challenges, financial constraints, regulatory hurdles, and organisational resistance. The findings underscore the need for a strategic approach that will help tackle all obstacles by realising the benefits of IoT-predictive maintenance in the railway sector. This study offers significant insights for stakeholders, offering a deep understanding of the challenges of IoT-based predictive maintenance in railways. Future research directions are suggested, emphasising the importance of long-term studies, holistic approaches, and the integration of emerging technologies to address the identified barriers.16 0Item Restricted Explaining Machine Learning Classifiers For Android Malware Detection(King's College London, 2024-08-03) Bin Hazzaa, Zaid; Pierazzi, FabioThe prevalence of Android malware continues to rise, and traditional approaches are proving ineffective against the evolving tactics of direct attacks. Manually inspecting applications is no longer a practical solution. Machine learning has demonstrated success in various domains, and its high performance in Android malware detection positions it to be effectively deployed in real-world scenarios. However, real-world results have yet to align with experimental findings, and the unique requirements of the security field have led to a lack of trust in its practical application. This research aims to address this issue by utilizing best practices for conducting experiments to eliminate experimental bias and employing explanation methods to enhance the transparency and robustness of the classifier. These measures are critical for building trust among security experts, with transparent, learning-based malware detection being a paramount necessity in the security system. Providing thorough explanations is key to informed decision- making. The research utilizes activities, services and receivers feature sets from Drebin feature extraction to explore the significance of feature sets and employs explanation methods to gain deeper insights into the model.13 0Item Restricted The Complexity of The System & Decisions Through A Digital Participatory Approach(UCL, 2024-07) Khayat, Abdulaziz; Philippe, MorelThis report explores the potential of using computational tools to reinterpret the legal text of the city and trace its impact. It discusses the laws, regulations, and decision-making processes to construct cities drifting away from bureaucratic arbitrary existing city governance and decision-making models. To develop such a system for governing and making urban decisions in cities, first, we must understand the nature of cities. Analyzing the city will shed light on the complexity of its components and its nature being a multi-faceted organism. Hence, the first section contains literature reviews of prominent works of major architectural figures to uncover the bureaucratic narrative of architecture. In the second section, multiple approaches to understanding cities are discussed. The third section explores the computational potential of participatory planning. The fourth section explores the concept of text similarity in machine learning and the urban environment. Finally, the last section demonstrates the application of the discussed tools and concepts.7 0Item Restricted Evaluation of Use of Artificial Intelligence (AI) and Machine Learning to Practice and Master Colonoscopic Skills.(University of Dundee, 2024-08) Alshahrani, Norah Abdullah; Tang, BenjieAbstract Objective The abstract concisely summarizes the research project "Evaluation of Use of Artificial Intelligence (AI) and Machine Learning to Practice and Master Colonoscopic Skills." It outlines the background of flexible colonoscopy, highlighting its importance in diagnosing and treating colorectal diseases. The study emphasizes the potential of VR simulators to provide a safe, controlled training environment. It identifies the need for quantitative data defining the number of procedures required to achieve competence in VR training. The research aims to demonstrate the effect of the use of AI and machine learning in colonoscopy traning.by conducting experiments with novice subjects and collecting and analyzing data. The expected outcome is to provide quantified evidence supporting the use of VR and AI in colonoscopy training, ultimately improving training methods and enhancing patient safety. Methods The methodology of this study involves a mixed approach where novice subjects undergo hands-on training on VR colonoscopy systems. Participants are selected based on specific criteria, and consent is obtained before involvement. The study utilises a VR simulator alongside physical phantom models to ensure comprehensive training. Detailed experimental procedures are followed, including simulation-based training sessions and performance assessments. Data is collected systematically through observation, performance metrics, and feedback and analysed using statistical methods such as SPSS to quantify the proficiency-gain curve and evaluate the effectiveness of VR training in mastering colonoscopic skills. Results This study included colonoscopy examinations performed on eight volunteers four times and compared with four experts who were examined 500 times. The results indicated that the average time taken to complete the procedure varied between (5:03 to 13:10 minutes) and the time to reach the cecum (4:58 to 10:10 minutes), with statistically significant differences between volunteers (P = 0.03) in the time to reach the cecum. The comparison between the expert group and volunteers also showed statistically significant differences between experts and volunteers in some aspects, such as the time taken to reach the cecum (2:22 minutes for experts versus 7:37 minutes for volunteers). Although the percentage of time in which a clear vision was maintained was higher among experts (96.75%) compared to volunteers (92.62%), this percentage among volunteers was also statistically significant, reflecting the importance of training and practice in improving this skill. Conclusion The conclusion of this study indicates that using VR simulators and AI in colonoscopy training significantly enhances skill acquisition, reduces the proficiency-gain curve, and ensures a safer training environment. The data analysis shows a marked improvement in performance among novice subjects trained with VR, validating the effectiveness of this approach. The study provides quantified evidence supporting the integration of VR and AI technologies in medical training programs, suggesting that such methods effective.9 0Item Restricted Diagnosis of Oral and maxillofacial cysts using artificial intelligence: a literature review(University of Manchester, 2024) Almohawis, Alhaitham; Yong, SinAbstract Oral and maxillofacial cysts are cavities that can pose significant risks if not detected and treated promptly. Many of these cysts are asymptomatic, often going unnoticed until complications arise. The introduction of artificial intelligence (AI) presents a promising opportunity for early detection and management of these cysts. Aim: To explore current studies on the use of artificial intelligence in diagnosing oral and maxillofacial cysts. Objectives: To examine the existing literature in this field, assess the accuracy, effectiveness, and limitations of AI models, and identify challenges in implementing AI in clinical practice. Methods: This literature review followed a systematic approach, identifying 223 studies from PUBMED and SCOPUS databases between 1975 and 2024. After applying inclusion and exclusion criteria, 26 retrospective cohort studies were included in the final analysis. A risk of bias assessment was conducted using the ROBINS I tool. Results: The investigation revealed that AI models consistently demonstrate high accuracy in detecting oral cysts in both radiographs and digital histopathology. The ROBINS I tool indicated a moderate risk of bias in most of the included studies. Notable limitations include limited datasets, variable data quality, and a lack of explainability in AI models results. Conclusion: AI models have shown considerable effectiveness and speed in detecting both simple and complex cysts. However, to fully leverage AI's potential in clinical settings, further rigorous studies are needed to evaluate its risks, benefits, and feasibility, ensuring compliance with governmental health regulations on AI.13 0Item Restricted Exploring the Applications of Artificial Intelligence in Enhancing Pre-Hospital Care: A Scoping Review(Queen’s University, Belfast, 2024) Alfaifi, Yahya; Clarke, SusanArtificial Intelligence (AI) has the potential to significantly improve pre-hospital care, especially in emergency medical services (EMS). However, its current application remains scattered, with varying integration levels across care stages. This scoping review aims to map and assess existing research on AI applications within pre-hospital care without focusing on specific AI technologies, such as machine learning (ML), deep learning (DL), or decision support systems (DSS). The review reflects the current research landscape, capturing how AI is utilised across critical stages such as call-taking, dispatch, and on-scene assessment. Using the framework developed by Arksey and O’Malley (2005), a systematic search was conducted across multiple databases to identify studies relevant to AI in pre-hospital care. The scope was deliberately broad to capture a comprehensive view of the available literature, focusing on identifying areas where further research is needed. The findings indicate that DSS is commonly used to support decision-making in call-taking and dispatch, while more advanced AI applications like ML and DL show potential in predictive analytics and real-time decision-making. However, these technologies are still in their early stages of real-world implementation. This review highlights the gaps in AI research, particularly in the later stages of prehospital care, such as transport and handover. Further exploration is necessary to unlock AI’s full potential in enhancing EMS operations and outcomes.46 0Item Restricted The Role of Artificial Intelligence in Breast Cancer Screening Programmes: A Literature Review and Focus Upon Policy Implications(The University of Edinburgh, 2024) Alrabiah, Alanoud; Hellowell, MarkBackground: Breast cancer (BC) is a leading cause of morbidity and mortality amongst older women, leading to the introduction of screening programmes to support earlier detection and improved survivability. Current screening programmes rely upon the performance of radiologists in terms of accuracy; however, evidence shows that both under and overdiagnosis means screening also results in harms to some women. Artificial intelligence is then a promising technology for improving the accuracy of mammogram screening. Aim: To describe the potential roles of AI in BC screening, and the potential benefits, limitations and risks in these roles. Methods: PubMed, SCOPUS, and CINAHL were searched. Primary research studies published in English and in the last ten years, investigating the accuracy of AI systems for screening BC, were eligible for review. Evidence was appraised using the CASP (2024) checklists and data analysed narratively. Results: 14 studies were found eligible for review, mostly adopting a retrospective study design or laboratory study design. Roles for AI in BC screening include as a standalone system replacing radiologists entirely, as risk stratification systems used before radiologist readings, or as reader aids. While some studies reported AI systems to be superior, others reported accuracy to be inferior to radiologist readings. Differences in results could be due to variations in AI system or radiologist performance. Conclusion: There is insufficient evidence to support the use of AI in BC screening programmes, and more robust, prospective studies comparing readings from clinical practice are urgently required. Policy must also be implemented to regulate the use of AI until there is sufficient evidence to support its use.12 0Item Restricted Exploring the Potential of Thermographic Data and Machine Learning for Defect Detection in Automated Fiber Placement (AFP)(University of Strathclyde, 2024-02-06) Alsubaei, Obaid Fadghosh; Yang, Erfu(Part1) The increasing demand for composites in the industry has led to the development of new automated manufacturing techniques such as Automated Fiber Placement (AFP). However, owing to some factors, small defects can occur during the AFP process which may compromise the structural integrity of the product. Traditionally these defects are detected manually by inaccurate and time-consuming inspection methods. In order to resolve this problem, it is necessary to implement effective automated defect detection systems. As an attempt to contribute to the solution of the problem, this paper reviews the current state of automated defect detection in AFP, and explores the potential of machine learning for improving defect detection. The review provides background information on composite materials, traditional and automated composite manufacturing methods, and defects associated with AFP. It also discusses traditional inspection techniques used in AFP and existing techniques for automated defect detection, in particular machine learning-based techniques. In addition, it also covers different data acquisition approaches for training ML algorithms. The paper aims to contribute towards advancing automated defect detection in AFP by utilizing machine learning techniques coupled with thermographic datasets in order to offer fast, accurate and cost-effective defect detection methods.41 0