Aolin Ding

Rutgers Sexual and Reproductive Health and Rights, Accenture (Switzerland)

Papers

Total Citations

H-Index

About

Aolin Ding is a leading researcher in the security of robotic aerial vehicles (RAVs), specializing in the intersection of cyber-physical systems, embedded firmware, and approximate computing. Their work addresses the critical challenge of protecting drone controllers from increasingly sophisticated cyber-physical attacks. Ding’s most influential contribution is the development of "Mini-Me," a data-driven security framework that leverages deep neural network-based approximate computing to detect and mitigate attacks on RAV controllers—a paper that has garnered 24 citations for its novel approach to balancing security with real-time performance constraints. They have also pioneered methods for reverse engineering and retrofitting closed-source RAV control firmware using a technique called DISPATCH (13 citations), enabling security analysis of proprietary systems that were previously opaque. More recently, Ding introduced a data-driven vulnerability assessment methodology (8 citations) that automates cyber-physical testing for RAVs, addressing the growing complexity of attack surfaces in modern drone software. Their work is notable for bridging the gap between theoretical security and practical deployment, offering tangible tools for securing UAV-as-a-Service platforms. Ding’s research is essential reading for anyone working on embedded system security, drone forensics, or resilient autonomous systems.