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About

Diane Gan is a prominent researcher specializing in cybersecurity for cyber-physical systems, with a particular focus on intrusion detection in autonomous and robotic vehicles. Her work addresses one of the most pressing challenges at the intersection of artificial intelligence and vehicular security: developing robust, adaptive mechanisms to detect and counter cyber attacks on resource-constrained mobile platforms. Gan's most influential contribution, "Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning" (2017, 270 citations), pioneered the use of computational offloading to overcome processing limitations inherent in vehicle systems, enabling sophisticated deep learning-based threat detection. This work fundamentally reframed how the research community approaches vehicular cybersecurity. Her earlier studies explored decision tree methods, Bayesian networks, and behaviour-based anomaly detection techniques applied to robotic vehicles and drones, collectively accumulating over 250 additional citations and demonstrating a systematic, multi-methodological approach to the field. Notably, Gan's research extends to emergency response robotics, examining physical indicators of cyber attacks on rescue robots — highlighting the potentially life-critical consequences of security vulnerabilities. Her body of work has been instrumental in establishing cyber-physical intrusion detection as a dedicated and vital research discipline, making her an essential reference point for scholars and engineers working in autonomous systems security.