Saudi Cultural Missions Theses & Dissertations
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Item Restricted AUTOMATED DETECTION OF OFFENSIVE TEXTS BASED ON ENSEMBLE LEARNING AND HYBRID DEEP LEARNING TECHNIQUES(Florida Atlantic University, 2025-05) Alqahtani, Abdulkarim Faraj; Ilyas, MohammadThe impact of communication through social media is currently considered a significant social issue. This issue can lead to inappropriate behavior using social media, which is referred to as cyberbullying. The accessibility and freedom of expression afforded by social media platforms enable individuals to share their emotions and opinions, but it also leads to cyberbullying behavior. Automated systems are capable of efficiently identifying cyberbullying and performing sentiment analysis on social media platforms. In this dissertation, our focus is on enhancing a system to detect cyberbullying in various ways. Therefore, we apply natural language processing techniques utilizing artificial intelligence algorithms to identify offensive texts in various public datasets. The first approach leverages two deep learning models to classify a large-scale dataset, combining two techniques: data augmentation and the GloVe pre-trained word representation method to improve training performance. In addition, we utilized multi-classification algorithms on a cyberbullying dataset to identify six types of cyberbullying tweets. Our approach achieved high accuracy, particularly with TF-IDF (bigram) feature extraction, compared to previous experiments and traditional machine learning algorithms applied to the same dataset. We employed two ensemble machine learning methods with the TF-IDF feature extraction technique, which demonstrated superior classification performance. Moreover, we used four feature extraction methods, BoW, TF-IDF, Word2Vec, and GloVe, to determine which works best with the ensemble technique. Finally, we utilize a multi-channel convolutional neural network (CNN) enhanced with an attention mechanism and optimized using a focal loss function.16 0Item 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 Feature extraction for high dimensional healthcare data(University of Surrey, 2024-02-19) Alanazi, Bader Bander D; Kouchaki, SamanehABSTRACT In the contemporary era of digital technology, the healthcare sector is faced with an abun-dance of huge databases, mostly due to the widespread adoption of machine learning and data mining methodologies. Nevertheless, the substantial complexity of large datasets pre-sents notable obstacles, such as the predicament known as the 'curse of dimensionality'. The primary objective of this project is to tackle these issues by formulating methodologies that enable the automated extraction of characteristics from complex Intensive Care Unit (ICU) data, which consists of numerous dimensions. The ultimate aim is to utilise these methodol-ogies to anticipate the likelihood of in-hospital death following admission to the ICU. The utilises a variety of advanced feature extraction methods, encompassing both linear and nonlinear approaches such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Autoencod-ers. The aforementioned methodologies are employed on the MIMIC III dataset, encompassing data pertaining to a population of around fifty-one thousand patients. Every patient can be identified by their distinct admission identification number. The primary objective of this study is to assess methodologies for the automated extraction of features that can be subsequently employed in healthcare applications. The study addi-tionally investigates the potential of employing more sophisticated and advanced machine learning models, such as deep learning models, to effectively capture intricate patterns and relationships within the data characterised by a high number of dimensions. Further could explore the practical application of these extracted traits in real-world healthcare contexts, perhaps resulting in the development of more precise and efficient predictive models and enhanced patient outcomes. This study makes a valuable contribution to the domain of machine learning in the healthcare sector, with a specific focus on the automated extraction of features from complex datasets to predict in-hospital mortality. The results of this study have the potential to contribute to the progress of data-driven solutions in the field of healthcare.13 0