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.