Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted An NLP-Driven Framework for Business Email Compromise Detection and Authorship Verifcation(Saudi Digital Library, 2025) Almutairi, Amirah; AlHashimy, Nawfal; Kang, BooJoongBusiness Email Compromise (BEC) presents a critical cybersecurity threat, leveraging linguistic impersonation and social engineering rather than traditional malicious payloads. These attacks routinely evade conventional flters by mimicking legitimate communication styles and exploiting trusted identities. This thesis explores content-based detection strategies for BEC using a sequence of natural language processing (NLP) models. First, it proposes a transformer-based classifer to detect semantic indicators of deception in email body text. Second, it develops a Siamese authorship verifcation (AV) model that captures stylistic consistency, even under adversarial mimicry. These components are unifed within a multi-task learning (MTL) framework that simultaneously optimizes for BEC detection and AV by sharing underlying representations while preserving task-specifc objectives. To support empirical evaluation, a structured taxonomy of BEC fraud is introduced, and a synthetic email dataset is generated through prompt-guided language model fne-tuning and human validation. Experiments on combined real and synthetic corpora demonstrate that the MTL model achieves up to 97% F1-score in BEC detection and 93% in AV, outperforming transfer learning baseline while reducing false positives and computational overhead. This work contributes a principled, modular, and extensible framework for enhancing email security through joint semantic and stylistic analysis, addressing gaps in current defenses against sophisticated impersonation attacks.12 0Item Restricted Pseudo-Labeling for Deep Learning-Based Side-Channel Disassembly Using Contextual Layer and Feature Engineering(Saudi Digital Library, 2025) Alabdulwahab, Saleh Sami S; Son, YunsikEmbedded devices face critical cyber-attacks due to their lightweight design and the sensitive data they handle. Integrating cloud and embedded systems increases the need for security measures against threats. Among these threats are deep learning-based side-channel disassembly attacks, which can expose sensitive information or steal software intellectual properties. Conducting a security test to evaluate the systems against these threats is essential. However, the main challenges include a comprehensive and refined dataset for training deep learning-based side-channel attacks and the lack of public datasets; labeling and profiling such attacks are costly and time-consuming. Additionally, accurately disassembling a single instruction is difficult due to the multiple classes representing each instruction and the obfuscation caused by dummy instructions. This study aimed to create an advanced side-channel evaluation methodology that performs three main deep-learning tasks: profiling using context-aware pseudo-labeling techniques at an instruction level, a disassembly model enhanced with moving log-transformed temporal interaction features, and a sequence labeling model for the detection of dummy instructions using natural language processing techniques. Utilizing gated recurrent units, the proposed pseudo-labeling model achieved 0.996 R2 in estimating the power trace for the assembly instructions. The proposed features improved the disassembly model's accuracy to 0.993, outperforming the related works. Additionally, the detection of dummy instructions using a long short-term memory model reached an accuracy of 0.979. This study provides valuable insights and methodology for measuring the software robustness against side-channel attacks.16 0Item Restricted A CODE-MIXING TRANSLITERATION MODEL TO IMPROVE HATE SPEECH DETECTION IN THE SAUDI DIALECT TWEETS(Universiti Malaya, 2024) Alhazmi, Ali Hamoud H; Associate Norisma Binti Idris, Associate Rohana Binti Mahmud, Nurul Binti Japar Mohamed Elhag Mohamed AboTechnological developments over the past few decades have changed the way people communicate, with platforms like social media and blogs becoming vital channels for international conversation. Even though hate speech is vigorously suppressed on social media, it is still a concern that needs to be constantly recognized and observed. Although great efforts have been made in this area for English-language social media content, but for Arabic language, the detection of hate speech still has many specific difficulties. Arabic calls for particular consideration when it comes to hate speech detection, because of its many dialects and linguistic nuances. Another degree of complication is added by the widespread practice of "code-mixing," in which users merge various languages smoothly. Recognizing this research vacuum, the study aims to close it by examining how well machine learning models containing variation features can detect hate speech, especially when it comes to Arabic tweets featuring code-mixing. Therefore, the objective of this study is to assess and compare the effectiveness of different features and machine learning models for hate speech detection on Arabic hate speech emoji, and code-mixing hate speech datasets. To achieve the objectives, the methodology used includes data collection, data pre-processing, feature extraction, the construction of classification models, and the evaluation of the constructed classification models. The findings from the analysis revealed that the Term Frequency-Inverse Document Frequency (TF-IDF) feature, when employed with the Stochastic Gradient Descent (SGD) model, attained the highest accuracy, reaching 98.21% on code-mixing transliteration dataset. The findings from the analysis also revealed that the highest accuracy of 99% was attained on emoji transliteration dataset. Subsequently, these results were contrasted with outcomes from three baseline studies, and the proposed transliteration learning model on both the code mixing and emoji outperformed them, underscoring the significance of the proposed models. Consequently, this study carries practical implications and serves as a foundational exploration in the realm of automated hate speech detection in text.39 0Item Restricted Evaluating Text Summarization with Goal-Oriented Metrics: A Case Study using Large Language Models (LLMs) and Empowered GQM(University of Birmingham, 2024-09) Altamimi, Rana; Bahsoon, RamiThis study evaluates the performance of Large Language Models (LLMs) in dialogue summarization tasks, focusing on Gemma and Flan-T5. Employing a mixed-methods approach, we utilized the SAMSum dataset and developed an enhanced Goal-Question-Metric (GQM) framework for comprehensive assessment. Our evaluation combined traditional quantitative metrics (ROUGE, BLEU) with qualitative assessments performed by GPT-4, addressing multiple dimensions of summary quality. Results revealed that Flan-T5 consistently outperformed Gemma across both quantitative and qualitative metrics. Flan-T5 excelled in lexical overlap measures (ROUGE-1: 53.03, BLEU: 13.91) and demonstrated superior performance in qualitative assessments, particularly in conciseness (81.84/100) and coherence (77.89/100). Gemma, while showing competence, lagged behind Flan-T5 in most metrics. This study highlights the effectiveness of Flan-T5 in dialogue summarization tasks and underscores the importance of a multi-faceted evaluation approach in assessing LLM performance. Our findings suggest that future developments in this field should focus on enhancing lexical fidelity and higher-level qualities such as coherence and conciseness. This study contributes to the growing body of research on LLM evaluation and offers insights for improving dialogue summarization techniques.32 0Item Restricted Applications and effectiveness of using artificial intelligence in the assessment of individuals with aphasia, A scoping review(Saudi Digital Library, 2023-11-03) Alhejji, Bader; Caroline, Haw; Stuart, CunninghamAim: This review aims to examine the current uses of artificial intelligence (AI) in evaluating individuals with aphasia and assessing the effectiveness of these AI-based tools. Methodology: The scoping review methodology was employed to comprehensively investigate the role of artificial intelligence (AI) and machine learning in aphasia assessment. This encompassed systematic searches across databases such as ScienceOpen and PubMed, supplemented by Google Scholar and StarPlus for grey literature inclusion. Eligibility criteria targeted machine learning and AI techniques applied to adult aphasia patients, with a focus on post-2010 publications. The data extraction process involved documenting study particulars, AI algorithms, outcome measures, and findings. Descriptive analysis and statistical methods facilitated AI approach identification, categorization, and accuracy assessment. The PRISMA checklist was utilized for study quality evaluation, promoting transparency and rigor. Findings: Predominant AI approaches within this review were Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), prominently featured across the selected studies. AI-based tools for the assessment of aphasia exhibited an average effectiveness of 88.3%, drawn from insights gleaned from 24 studies with 1546 participants. Remarkably, one of the AI apps achieved an accuracy of 95%, underscoring SVMs and CNNs' technology potential for accurate and impactful aphasia assessment outcomes. These findings emphasize the effectiveness and capacity of SVMs and CNNs to enrich clinical practice and expand research in aphasia evaluation. Conclusion: The present study identifies AI-based systems for assessing aphasia as a promising field, yet it acknowledges limitations concerning patient privacy and the need for a comprehensive AI-based system that covers the entire assessment process of aphasia.31 0