Dr Mohammed Hassanin
Lecturer
Doctor of Philosophy (PhD) in Information Technology
University of New South Wales (UNSW Canberra), Australia
2022
Thesis: Securing Visual Applications Against Adversarial Attacks
Master of Computer Science
Cairo University, Egypt
2014
Thesis: Mobility Prediction for Network Communications
Bachelor of Computer Science
Zagazig University, Egypt
2007
UNSW Canberra
School of Systems & Computing
Dr Mohammed Hassanin is a researcher and academic specialising in Cyber security, Artificial Intelligence (AI), Computer Vision, and Vision Security. He holds a PhD in Information Technology from the University of New South Wales (UNSW Canberra), where his doctoral research focused on securing visual applications against adversarial attacks.
Dr Hassanin has extensive research and teaching experience across Australian universities, including UNSW Canberra, the University of Canberra, and the University of South Australia. His research focuses on developing secure, and robust AI systems, with applications in computer vision, adversarial machine learning, cyber threat detection, and autonomous systems.
He has contributed to several externally funded research projects involving adversarial machine learning, satellite cybersecurity, intelligent cyber defence, and AI-powered safety technologies. His work includes developing deep learning models for visual recognition, human pose estimation, cyber threat detection, and secure communications.
Dr Hassanin has published in internationally recognised journals, including ACM Computing Surveys, Information Fusion, IEEE Transactions on Industrial Informatics, Ad Hoc Networks, and Signal, Image and Video Processing. His publications contribute to advances in visual intelligence, trustworthy AI, and cybersecurity.
Alongside his research, Dr Hassanin has taught undergraduate and postgraduate courses in artificial intelligence, computer vision, data science, network security, and cybersecurity.
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2025–2029 UC-Butterfly Healthcare Co-funded PhD Scholarship ($68,059.88 AUD)
Collaborator
2017 - Australian Government Research Training Program (RTP) Scholarship – UNSW Canberra
2014 - Master's Research Scholarship – Cairo University
2007 - Graduation Project Award – Zagazig University
Dr Mohammed Hassanin's research focuses on Cybersecurity, LLMs, Vision Security, and Trustworthy Machine Learning, with particular emphasis on the security, robustness, and reliability of AI-driven systems in safety-critical environments.
His research interests and activities encompass:
Cybersecurity and AI-Driven Threat Detection: Developing intelligent security frameworks using deep learning, federated learning, and large language models (LLMs) to identify, analyse, and mitigate cyber threats across interconnected systems, including satellite communications, IoT networks, and cyber-physical infrastructure.
Computer Vision and Visual System Security: Investigating adversarial attacks and defence mechanisms targeting computer vision models, including image recognition, object detection, visual perception, and autonomous decision-making systems.
Radar and Intelligent Sensor Security: Exploring the robustness and security of radar-based perception, sensor fusion, and AI-powered sensing technologies against adversarial manipulation, spoofing, and other emerging threats.
Electric Vehicle (EV) and Autonomous System Security: Investigating cybersecurity challenges affecting electric vehicles, connected mobility systems, autonomous driving technologies, and AI-based perception systems, with an emphasis on protecting intelligent sensing and decision-making processes.
Adversarial Machine Learning and Trustworthy AI: Designing robust learning algorithms and security mechanisms to improve AI resilience against adversarial attacks, data manipulation, and emerging vulnerabilities in intelligent systems.
Applied Cyber-Physical Security: Exploring security solutions for interconnected intelligent infrastructure, including intelligent transportation systems, connected vehicles, IoT ecosystems, and safety-critical AI applications.