Assistant Professor at Imam Mohammed bin Saud Islamic university, Saudi Arabia📖
Dr. Reham Abdulaziz Al-Dayil is an Assistant Professor at Imam Mohammed bin Saud Islamic University, specializing in computer engineering, cybersecurity, and artificial intelligence. With a strong academic and research background, she has contributed to cutting-edge advancements in open-set classification, remote sensing, and cyber threat detection. She has published extensively in prestigious journals and international conferences, focusing on machine learning applications in cybersecurity and geospatial analysis.
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Education Background🎓
Dr. Al-Dayil earned her Ph.D. in Computer Engineering from King Saud University (2017-2022), where she researched mobile botnet detection using artificial immune systems and user activity correlation. She completed her Master’s in Computer Engineering from King Saud University (2010-2015), with a thesis on social media-based botnet detection. Her academic journey began with a Bachelor’s degree in Computer Science from King Saud University (2002-2006), where she developed a License Plate Extraction System as her final project.
Professional Experience🌱
Dr. Al-Dayil has over 17 years of experience spanning academia and industry. Since 2023, she has been an Assistant Professor at Imam Mohammed bin Saud Islamic University, where she previously served as a Lecturer (2017-2023). Before that, she was a Teaching Assistant at Shaqra University (2009-2017), contributing to curriculum development and student mentorship. Her industry experience includes working as a Developer at AlFanar Company (2006-2009), where she gained hands-on expertise in software development, programming, and database systems. She has taught courses in data communication systems, networking, information security, web programming, database management, and digital logic design.
Dr. Al-Dayil’s research focuses on artificial intelligence, cybersecurity, machine learning, and remote sensing. Her work explores advanced methodologies for open-set classification, domain adaptation, and adversarial learning in cybersecurity. She has contributed to research on vision transformers for remote sensing image classification, cyber threat detection frameworks, and deep learning techniques for cross-scene classification.
Author Metrics
Publications Top Notes 📄
1. Vision Transformers for Remote Sensing Image Classification
- Authors: Y. Bazi, L. Bashmal, M. M. A. Rahhal, R. A. Dayil, N. A. Ajlan
- Journal: Remote Sensing, Volume 13, Issue 3, Article 516
- Year: 2021
- Citations: 460
- Summary: This study explores the use of Vision Transformers (ViTs) for remote sensing image classification, demonstrating their effectiveness in capturing spatial dependencies in satellite imagery compared to traditional CNN models.
2. Deep Open-Set Domain Adaptation for Cross-Scene Classification Based on Adversarial Learning and Pareto Ranking
- Authors: R. Adayel, Y. Bazi, H. Alhichri, N. Alajlan
- Journal: Remote Sensing, Volume 12, Issue 11, Article 1716
- Year: 2020
- Citations: 34
- Summary: This research presents a novel deep learning framework using adversarial learning and Pareto ranking for open-set domain adaptation, improving classification performance in remote sensing applications with unseen data.
3. Detecting Social Media Mobile Botnets Using User Activity Correlation and Artificial Immune System
- Authors: R. A. Al-Dayil, M. H. Dahshan
- Conference: 2016 7th International Conference on Information and Communication Systems (ICICS)
- Year: 2016
- Citations: 10
- Summary: This paper introduces a botnet detection method leveraging user activity correlation and artificial immune systems to identify malicious activities on social media-based mobile networks.
4. Energy-Based Learning for Open-Set Classification in Remote Sensing Imagery
- Authors: M. M. Al Rahhal, Y. Bazi, R. Al-Dayil, B. M. Alwadei, N. Ammour, N. Alajlan
- Journal: International Journal of Remote Sensing, Volume 43, Issues 15-16, Pages 6027-6037
- Year: 2022
- Citations: 9
- Summary: The study introduces an energy-based learning approach to improve open-set classification in remote sensing imagery, enhancing the detection of unknown classes in satellite data.
5. Exploring Cybersecurity Metrics for Strategic Units: A Generic Framework for Future Work
- Authors: M. Arafah, S. H. Bakry, R. Al-Dayel, O. Faheem
- Book Chapter: Advances in Information and Communication: Proceedings of the 2019 Future of Information and Communication Conference
- Year: 2020
- Citations: 5
- Summary: This paper proposes a framework for cybersecurity metrics, offering insights into measuring and assessing security performance in strategic IT units.
Conclusion
Dr. Reham Abdulaziz Al-Dayil is an exceptional candidate for the Best Researcher Award due to her high-impact publications, interdisciplinary expertise, strong academic presence, and contributions to AI-driven cybersecurity and remote sensing. With continued focus on industry collaborations, research funding, and public engagement, she can further elevate her global impact in research.