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Effat University Institutional Repository
Welcome to the Effat University Repository, a service of Effat Library and Cultural Museum. This repository provides global and open access to scholarship and research output created by Effat University faculty, graduate and undergraduate students.
While the University Repository is accessible to the public, submission is limited to the Effat University community.
To submit your research, click here.
If you have any questions, please email us at repository@effatuniversity.edu.sa
Communities in Effat University Repository
Select a community to browse its collections.
Recently Added
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AI-Based Intrusion Detection System (IDS) for Advanced Persistent Threat (APT) DetectionAbstract: This project presents an innovative anomaly-based Intrusion Detection System (IDS) for detecting Advanced Persistent Threats (APTs) using artificial intelligence. It integrates three models—Support Vector Machines (SVM), Multi- Layer Perceptrons (MLP), and Long Short-Term Memory (LSTM)—through a voting mechanism to improve accuracy and reduce false positives. The system uses a robust dataset, DAPT 2020, and applies data preprocessing techniques such as feature extraction and normalization to enhance model performance. Training and optimization of these models were conducted to ensure high precision, recall, and F1-scores. The IDS is deployed in a scalable cloud environment for real-time network traffic monitoring, paired with a user-friendly interface to facilitate usability. This approach supports proactive detection of sophisticated cyber threats, aligning with Vision 2030’s digital transformation goals and SDG 9 by enhancing infrastructure security and fostering innovation.
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Detecting Characteristics Based on Keystroke Dynamics Analysis Using Machine LearningThis project aims to detect several characteristics of an individual based on their typing behavior. Data is collected through a pre-programmed online keyboard, capturing metrics such as flight time, key press and release timing, and other keystroke dynamics. The collected dataset is analyzed using machine learning algorithms to identify distinct typing patterns and extract relevant features. Based on this analysis, the trained machine learning model classifies individuals into two categories: above or under the age of 18. This classification approach leverages typing behavior to provide an innovative method for age-based categorization, with potential applications in user authentication and personalization.
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Investigating the Hurdles to Urban Farming in the Saudi Arabia Cities: An Analysis of the Constraints and Solutions Across Key RegionsSustainability is a cornerstone of urban development, emphasizing the enhancement and preservation of systems to meet present and future needs. This concept is built upon three interconnected pillars: environmental sustainability, social sustainability, and economic sustainability. These elements collectively form the foundation for cities' development plans, aiming at residents' quality of life. By 2030, the United Nations aims to achieve 17 Sustainable Development Goals (SDGs), at least half directly linked to food and environmental security (United Nations, n.d.). According to the Food and Agriculture Organization (FAO), approximately 68% of the world’s population is expected to reside in urban areas by 2050, necessitating more efficient and sustainable food systems encompassing production, processing, consumption, recycling, and waste management (FAO, 2018). Urban farming, as a form of agricultural practice, can potentially equip urban and peri-urban areas with sustainable food resources for city dwellers. This thesis investigates the barriers to adopting the urban farming concept within the Saudi Arabian context, focusing on the Western Province due to its unique characteristics and significance as a case study region. The research employs a mixed-methods approach, integrating qualitative and quantitative analyses to ensure a comprehensive understanding of urban farming practices. Qualitative data includes literature reviews and semi-structured interviews with key stakeholders, while quantitative data is drawn from governmental resources and online surveys. The study aims to identify major constraints, propose adaptation strategies for policymakers and stakeholders, and prioritize solutions based on empirical findings. The research is structured around key elements, including the problem definition, objectives, methodology, scope, and limitations. Expected outcomes include the development of a framework for analyzing urban farming challenges in different regions of Saudi Arabia, actionable recommendations for integrating urban farming into urban planning strategies, and alignment with broader sustainable urban development goals.
