SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Enhancing Network Security through Machine Learning and Threat Intelligence Integration in Next-Generation Firewall IDS/IPS Systems(Northumbria University, 2024-09-05) Sufi, Mohammed; Abosata, NassrThis dissertation explores how Machine Learning (ML) and real-time Threat Intelligence feeds can improve Next-Generation Firewall (NGFW) systems especially in increasing the accuracy and efficacy of Intrusion Detection and Prevention Systems which contribute in enhancing network security. Using threat intelligence feeds including IP addresses, domains, and URLs which come with related information’s such as the Indicators of Compromise (IoC) reputation scores, and threat categories like "malware" or "phishing,”. Thus, by using this information, applying supervised learning techniques enable to easily assess and classify threats into high-risk and low risk categories in order to reduce false positives, which result in enhancing threat detection and prevention accuracy. These classified threat feeds are dynamically updated, allowing the NGFW to protect against new threats by adjusting its security rules with appropriate countermeasures. The results show that combining ML with classified threat feeds improves the NGFW's capacity to detect and prevent threats, leading to more focused and responsive threat management.16 0Item Restricted Forecasting OPEC Basket Oil Price and Its Volatilities Using LSTM(University College London, 2024-09) Almazyad, Sulaiman; Hamadeh, LamaThe global economy is greatly affected by oil prices, which have an impact on everything from consumer goods prices to transportation expenses. Forecasting these prices accurately is crucial for energy security, company strategy, and economic planning. Traditional statistical models such as ARIMA and SARIMA have been used for such forecasts, but struggle with the non-linear patterns inherent in oil price movements. This research explores the use of Long Short-Term Memory (LSTM) networks, a specialized form of Recurrent Neural Network (RNN) built to manage longterm dependencies, in predicting the OPEC reference basket oil price and its associated volatility, ultimately improving the accuracy of these forecasts. The model is built upon historical datasets of the OPEC Reference Basket (ORB), and its efficacy is assessed using a variety of performance indicators, including RMSE, MAE, and MAPE. The outcomes reveal that the LSTM model is4 0Item Restricted Credit Card Fraud Prediction Using Machine Learning Model(University of Essex, 2024-08) Alanazi, Mohammed; Walton, MichaelThe widespread adoption of credit cards has significantly increased the frequency of fraudulent activities. This has resulted in considerable financial losses for both consumers and financial institutions. As the use of credit cards continues to grow, the challenge of protecting transactions against unauthorized access has become more serious than ever. This research focuses on creating a solution using machine learning to accurately and effectively identify fraudulent credit card transactions. It addresses the issue of uneven transaction data by employing advanced methods such as logistic regression, XGBoost, LightGBM, and a hybrid model. The research involves thorough data preparation, model development, and careful assessment using measures “such as accuracy, precision, recall, F1 score, and ROC AUC”. This research leverages sophisticated machine learning techniques and tackles the specific challenges associated with imbalanced data. The study aims to significantly enhance the detection of fraudulent transactions while reducing false positives. The ultimate goal is to boost the security of financial systems, thus providing better protection against fraud, and to improve trust and reliability in credit card transactions.44 0Item Restricted Leveraging Brain-Computer Interface Technology to Interpret Intentions and Enable Cognitive Human-Computer Interaction(Univeristy of Manchester, 2024) Alsaddique, Luay; Breitling, RainerIn this paper, I present the developed, integration, and evaluation of a Brain–Computer Interface (BCI) system which showcases the accessibility and usability of a BCI head- set to interact external devices and services. The paper initially provides a detailed survey of the history of BCI technology and gives a comprehensive overview of BCI paradigms and the underpinning biology of the brain, current BCI technologies, recent advances in the field, the BCI headset market, and prospective applications of the technology. The research focuses on leveraging BCI headsets within a BCI platform to interface with these external end-points through the Motor Imagery BCI paradigm. I present the design, implementation, and evaluation of a fully functioning, efficient, and versatile BCI system which can trigger real-world commands in devices and digital services. The BCI system demonstrates its versatility through use cases such as control- ling IoT devices, infrared (IR) based devices, and interacting with advanced language models. The system’s performance was quantified across various conditions, achiev- ing detection probabilities exceeding 95%, with latency as low as 1.4 seconds when hosted on a laptop and 2.1 seconds when hosted on a Raspberry Pi. The paper concludes with a detailed analysis of the limitations and potential im- provements of the newly developed system, and its implications for possible appli- cations. It also includes a comparative evaluation of latency, power efficiency, and usability, when hosting the BCI system on a laptop versus a Raspberry Pi.7 0Item Restricted A Peer-to-Peer Federated Learning Framework for Intrusion Detection in Autonomous Vehicles(Lancaster University, 2024-09) Alotaibi, Bassam; Bradbury, MatthewAs autonomous vehicles (AVs) increasingly rely on interconnected systems for enhanced functionality, they also face heightened cyberattack vulnerability. This study introduces a decentralized peer-to-peer federated learning framework to improve intrusion detection in AV environments while preserving data privacy. A novel soft-reordering one-dimensional Convolutional Neural Network (SR-1CNN) is proposed as the detection engine, capable of identifying known and unknown threats with high accuracy. The framework allows vehicles to communicate directly in a mesh topology, sharing model parameters asynchronously, thus eliminating dependency on centralized servers and mitigating single points of failure. The SR-1CNN model was tested on two datasets: NSL-KDD and Car Hacking, under both independent and non-independent data distribution scenarios. The results demonstrate the model’s robustness, achieving detection accuracies of 94.39% on the NSL-KDD dataset and 99.97% on the Car Hacking dataset in independent settings while maintaining strong performance in non-independent configurations. These findings underline the framework’s potential to enhance cybersecurity in AV networks by addressing data heterogeneity and preserving user privacy. This research contributes to the field of AV security by offering a scalable, privacy-conscious intrusion detection solution. Future work will focus on optimizing the SR-1CNN architecture, exploring vertical federated learning approaches, and validating the framework in real-world autonomous vehicle environments to ensure its practical applicability and scalability.23 0Item Restricted Negative Mixture Models via Squaring Representation and Learning(University of Nottingham, 2024) Almhmadi, Samaher; Raykov, YordanThe truths behind a real-world data can be faced by measuring the uncertainty around data. From probabilistic view, the uncertainty is used with respected to unsupervised learning as learning objectives under the probability distributions and inference. Mixture models enhanced the expressiveness of probability distributions. Mixture models have provided a general framework used for clustering data by building more complex probability distributions. We are begging with discussion of mixture distributions and introduced the latent variable concept. Mixture types with respect to the number of components and its formulation are discussed. Some example of Gaussian mixture models is exposed. Mixture types with respect to mixture coefficient are also discussed. We exposed the statistical inference problem of mixture models with different approaches such as, latent variable models, Markov chain Mote Carlo method and variational methods. Through our discussion, we exposed a several illustrative examples. Some concepts of probabilistic circuits: representation, formulation and the corresponding inference are also discussed. In thesis, we applied probabilistic circuits in probabilistic inference. Also, we discussed how the negative mixture is presented as probabilistic circuits. And its structure as tractable computational graphs. Also, we discussed the representation for the squared negative mixture models as efficiently tensorized computational graphs. As well as how can reduces the model size under including negative parameters in this class of functions. Mixture models and especially negative mixture model via squaring to learn the truths of real data was discussed. Due to Gaussian mixture models applied in several branches of science such as machine learning, data mining, pattern recognition and statistical analysis. And Gaussian mixture model and negative Gaussian mixture model are an important subclass for learning in data. In this thesis, we focused on discussion these models in two cases positive and negative case. For the representing the valid negative mixture models, we discuss a generic strategy to support negative parameters called squaring a base mixture. And then, this framework is extending to probabilistic circuits. Finally, we discuss the main idea of my thesis The main aim of this thesis is discussion the inference problem in the framework of mixture models. As well as the basic role which play each of positive mixture model and negative weight mixture model, especially standard Gaussian mixture model and negative weight Gaussian mixture model in inference problem. we expose this thesis in five subsequent chapters describe as follows. In Chapter 1: We discuss mixture motivation and mixture types. Also, we expose to some standard mixture models. In Chapter 2: We discuss mixture types with respected to its coefficients. When mixture coefficient is reduced to negative values for some not all coefficients then mixture model called negative weight mixture model. Also, in this chapter expos to the statistical inference problem of mixture models with different approaches such as latent variable models, Markov chain Mote Carlo (MCMC) method and variational methods. In Chapter 3: We discuss the important ideas around the problem of probabilistic inference. Information about the class of queries to computing interesting quantities of a probability distribution are discussed and makes a family of probabilistic model tractable. Different illustrative examples are exposed. The probabilistic circuits: representation and inference were discussed. At the end of this chapter discussed negative MMs via squaring and representing negative MMs as probabilistic circuits. In Chapter 4: We discuss Gaussian mixture models used to present subpopulations within an overall population. Also, we have known how Gaussian mixtures which is constituted a form of unsupervised learning. In the second part, we discussed the negative weight Gaussian mixture models under negative coefficients which make it more expressive than Gaussian mixture models by reducing the number of components and parameters. Also, the comparison between standard Gaussian mixture model and negative weight Gaussian mixture model are formulated under a real example. In Chapter 5: We discuss the important contributions of positive and negative weight mixture models especially positive and negative weight Gaussian mixture models. As well as the future works which can be developed in mixture framework.71 0Item Restricted Utilizing Data Analytics for Fraud Detection and Prevention in Online Banking Systems of Saudi Arabia(University of Portsmouth, 2024-09) Almotairy, Yazeed; Jiacheng, TanThis thesis addresses the critical issues of online banking and online banking fraud in Saudi Arabia. The thesis focusses on the older methodologies of the online banking systems in Saudi Arabia. The frauds are discussed in detail that are occurring in the online banking systems and are causing inconvenience to the users and account holders of the online banks and applications. In this thesis, online banking frauds are discussed thoroughly, and the traditional fraud detection methods are elaborated as well. The vulnerabilities in the current systems are explored. It discusses how the older systems are not performing well and why the new system encompasses the power of data analytics and machine learning. The methods proposed use a set of data analytics and machine learning algorithms and techniques to detect fraud or any fraudulent activity that a scammer or fraudster may perform. The results of this study explain how the proposed system can outperform the traditional methodologies being used in Saudi Arabian online banking systems. The proposed system can also enhance the user experience. The possible privacy and ethical concerns are also discussed. In the end, it is also discussed what the future prospects are for the researchers who are looking to enhance this research or want to work in the field of data analytics and machine learning to improve the security of the security of online banking applications. In conclusion, this thesis not only contributes to the body of knowledge on online banking frauds in Saudi Arabia and their detection but also features future research topics for new researchers.Item Restricted Detecting LLM Generated Phishing Emails Using Machine Learning: A Multi-Classification Approach And A Comprehensive Evaluation(University of Birmingham, 2024-09) Alharthi, Alanoud; Andriotis, PanagiotisPhishing is a significant cybersecurity threat that targets organisations as well as individuals. The aim of this project is to provide a comprehensive machine learning model that can accurately detect LLM generated phishing with high accuracy from a dataset of four different classes of emails: LLM phishing, LLM non-phishing, Human phishing and Human non-phishing. This balanced and diverse dataset of 4000 emails acts as a real-world representation of the different types of emails that are sent daily that include different distinct features, allowing for an accurate feature differentiation from the classes of the dataset. The five machine learning algorithms that were used for this research are: Decision Tree, Support Vector Machine (SVM), Random Forest, Gradient Boost and K-Nearest Neighbours (KNN). These algorithms were tuned to evaluate the performance of the models after hyperparameter tuning. The highest accuracy achieved from the model before tuning was the SVM with an accuracy of 97.3%. The subsequent highly accurate models are Random Forest of 96.9%, KNN of 96.8% and Gradient Boosting of 96.7%. The model that achieved the lowest accuracy was Decision Tree, achieving an accuracy of 90.7%. Hyperparameter tuning was applied to models and the performance was re-evaluated to investigate if hyperparameter tuning enhanced the performance of the models. Other metrics such as precision, recall and F1-score were also measured. The developed and trained models were then integrated with a web page developed using streamlit for a user-friendly interface for the classifications of the emails. Overall, this research aims to provide a framework for detecting LLM phishing emails. The results of this research signify that with the correct methodologies, we can enhance the detection of LLM generated phishing, contributing to robust defences against emerging cyber threats.Item Restricted An Ontology-based Framework for the Modelling and Online Detection of Obsessive Compulsive Disorder(Cardiff University, 2024-11) muhajab, Areej; Abdelmoty, AliaIn the contemporary digital landscape, the prevalence and impact of Obsessive- Com- pulsive Disorder (OCD) discourse in online platforms have garnered increasing signif- icance. This thesis presents an integrated framework aimed at detecting and classi- fying OCD in online discourse by harnessing the synergy between ontology develop- ment and machine learning. The primary objective is to enhance the understanding and identification of OCD-related content within the vast and varied landscape of on- line forums. The research begins with the construction of a comprehensive ontology, named OCD, specifically designed to encapsulate the multifaceted aspects of OCD. This ontology is developed to represent the complex interplay of OCD symptoms, behaviors, and related mental health concepts. Drawing upon insights from medical literature, psy- chological studies, and existing biomedical ontologies, the OCD ontology provides a structured, hierarchical representation of OCD, enabling systematic identification and categorisation of OCD-related terms. Consequently, it furnishes a rich semantic framework that facilitates accurate interpretation of online discourse. In addition to ontology development, the thesis explores machine learning method- ologies, particularly focusing on the classification of OCD-related posts on online plat- form. A variety of classification models are employed to analyse and categorise online content. Leveraging the OCD ontology as a foundational reference for feature extrac- tion and semantic analysis, these models are trained and evaluated on a corpus of OCD forum posts. The classification process is designed to discern various OCD manifestations, such as obsessions and compulsions, thereby offering a granular un- derstanding of the disorder’s portrayal in digital communication. The outcomes of this thesis carry significant implications for mental health profes- sionals, online community moderators, and researchers. The developed framework and methodologies represent a pioneering tool for monitoring, understanding, and addressing OCD in the digital space.15 0Item Restricted Enhancing DDoS attack Detection using Machine Learning and Deep Learning Models(University of Warwick, 2023-09-26) AlObaidan, Fatimah; Raza, HassanTechnology has become an essential part of our daily lives, indispensable for both individuals and enterprises. It facilitates the exchange of an extensive range of information across different spaces. However, Internet security is a critical challenge in today's digital age with growing dependence on IT services. Thus, various network environments can be vulnerable to attacks, causing resource depletion and hindering support for legitimate users. One of these attacks is the Distributed Denial of Service (DDoS) attack. The nature of this type of attack is such that it impacts the availability of the system. The impact to confidentiality is primary due to threat actors using DDoS as method to create chaos whilst lunching cyber-attacks on other part of infrastructures. Therefore, it is essential that DDoS attacks required sharper focus from a research perspective. The network intrusion detection system (NIDSs) are important tool to detect and monitor the network environment from DDoS attacks. However, NIDS tools suffer from several limitation such as detecting new attack and misclassified attacks. Therefore, Machine Learning (ML) and Deep Learning (DL) models are increasingly being used for automated detection of DDoS attacks. While several related works deployed ML for NIDS, most of these approaches ignore the appropriate pre-processing and overfitting problem during the implementation of ML algorithms. As a result, it can impact the robustness of the anomaly detection system and lead to poor model performance for zero-day attacks. In this research study, the researcher is proposing a new ML and DL approach based on hybrid feature selection and appropriate pre-processing operation to classify the network flow into normal or DDoS attacks. The results of the experiments carried out by researcher suggest the efficiency and the reliability of the proposed lightweight models in achieving high detection rate while minimising the detection time with less number of features. This project complies with following two CyBOK Skills areas: Network Security: The project evaluates the network security and introduces efficient, lightweight models for DDoS attack detection. Security Operations and Incident Management: The project enhances incident management capabilities by crafting ML that monitors network flows within NIDS.11 0