Dr Tarek Gaber
School of Science, Engineering & Environment
Current positions
Senior Lecturer in Cyber Security
Biography
Tarek Gaber is a Senior Lecturer at the University of Salford (UK). He has over two decades of academic and research experience across cybersecurity, artificial intelligence (AI), secure systems, and Safe AI. His work focuses on developing resilient AI models, secure digital infrastructures, and innovative applications for industry and public sector transformation. Dr. Gaber has authored over 100 scholarly publications, including journal articles, conference papers, book chapters, and edited volumes — with more than 40 published in Q1 journals. He has led or co-led research projects exceeding £1 million in funding, supported by Innovate UK, GCHQ, Research England, and UKAEA. His research excellence has earned him recognition among Stanford University’s top 2% of scientists globally, and the #1 ranking in Computer Science at the University of Salford by the AD Scientific Index (2024). He has served as Programme Leader for the MSc Cyber Security programme at Salford, contributed to several Knowledge Transfer Partnerships (KTPs), and engaged in interdisciplinary projects with SMEs to deploy secure and explainable AI solutions. Dr. Gaber is a Fellow of the UK Higher Education Academy (FHEA), a member of IEEE, and frequently serves as a keynote speaker, journal reviewer, and editorial board member in his field.
Areas of Research
Cyber Security, Machine Learning, Artificial Intelligence, Artificial Intelligence Security
Areas of Supervision
Cybersecurity, AI security, AI
Privacy and Network Security, Dependable Software Engineering, Software Quality Management.
Qualifications
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PhD
2007 - 2012
Recognitions
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Developing Cutting Edge Creativity and Innovative Practice
Publications
- A Blockchain-Based Fox Optimization Algorithm for Optimizing and Securing Electrical Vehicles Charging
- Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training
- Robust thermal face recognition for law enforcement using optimized deep features with new rough sets-based optimizer