Cybersecurity - AI/ML - Full-stack
I’m a cybersecurity graduate from Princess Sumaya University for Technology (PSUT) with a strong interest in AI, machine learning, secure system design, and automation.
A mix of security, backend, and DevOps tools I actually use.
Selected work that combines security, AI, and real deployment.
A machine learning model that predicts California housing prices using stratified sampling, feature engineering, data preprocessing, and regression models such as Linear Regression, Decision Trees, and Random Forests. Includes Grid Search optimization and evaluation on a dedicated test set.
Gained practical experience with AWS services (creating VPCs, launching EC2 servers, IAMs and security groups, S3 buckets, etc.). Built a strong understanding of AWS global infrastructure, shared responsibility model, and core architectural principles for designing scalable, secure cloud solutions
Bachelors
Princess Sumaya University for Technology
Oct 2021 – Sept 2025
GPA: GOOD
High School Diploma
College De La Salle, Freres
Oct 2018 – Jun 2021
A Levels in Physics, Math, and Chemistry
AWS Cloud Security
Amazon Web Services (AWS)
Jan 2025
AWS Cloud Foundations
Amazon Web Services (AWS)
Jan 2025
Papers, articles, or posts that show how you think about security and tech.
Durbovnic, Croatia
Unmanned Aerial Vehicles (UAVs) have become indispensable across domains such as surveillance, disaster management, and industrial inspection. However, their heavy reliance on Global Positioning System (GPS) signals exposes them to spoofing, jamming, and denial-of-service attacks, creating significant risks in critical operations. To address this limitation, we propose a secure vision-based navigation framework that integrates Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and efficient path-planning algorithms. A lightweight UNet architecture with a ResNet50 backbone was trained on aerial datasets, achieving a Dice coefficient of 0.9155 and a mean Intersection over Union (mIoU) of 0.8658, enabling robust segmentation of roads, buildings, terrain, and obstacles. SLAM, powered by SuperPoint feature detection and SuperGlue matching, demonstrated reduced drift and improved trajectory consistency compared to classical ORBbased methods. Path-planning experiments further showed that A* consistently outperformed Dijkstra in real-time navigation scenarios. The integration of CNN-driven perception with SLAMbased localization provides reliable cost-fused maps for autonomous decision-making. This framework enhances confidentiality, integrity, and availability in UAV navigation, offering a scalable GPS-independent solution for secure deployment in contested and GPS-denied environments.
Nov 2025
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