معرفی
Qusay H. Mahmoud is affiliated with the University of Ontario Institute of Technology, Canada, and previously held roles at the University of Guelph (2002–2013) and completed his PhD at Middlesex University, London (2002). His research focuses on cybersecurity, machine learning, IoT systems, blockchain, and smart policing. He has authored over 200 peer-reviewed publications in journals like IEEE Access, Sensors, and Future Internet, and conferences such as Canadian AI, IEEE CCWC, and SMC. His work spans anomaly detection in IoT networks, adversarial machine learning, cryptocurrency fraud analysis, and reinforcement learning applications.
Research Interests: Dr. Mahmoud’s expertise includes developing AI-driven solutions for cybersecurity challenges, IoT security frameworks, blockchain applications in healthcare and finance, and enhancing smart policing through machine learning. He also explores adversarial training for robust deep learning models and reinforcement learning for bridging virtual-physical environment gaps. His contributions emphasize real-world applications, such as phishing detection, fault-injection attack mitigation, and cryptocurrency market analysis.
Articles Trends: Recent work highlights the use of large language models (LLMs) in crime prediction, adversarial robustness in GANs, and reinforcement learning for robotics. His publications often intersect machine learning with cybersecurity, IoT, and blockchain, addressing practical challenges like anomaly detection, fraud prevention, and system reliability.
Grants & Advising: While specific grants are not detailed, his extensive publication record suggests sustained research activity. No formal student advisees are listed, though collaborations with co-authors like Akramul Azim and Michael Lescisin indicate active teamwork.
Labs/Teams: Engaged in interdisciplinary projects at the University of Ontario Institute of Technology, focusing on AI, cybersecurity, and IoT systems. Collaborations extend to institutions globally, addressing emerging tech challenges like blockchain in healthcare and edge computing security.
