#322: Enhancing Network-edge Connectivity and Computation Security in Drone Video Analytics

A. E. Morel, D. K. Ufuktepe, R. Ignatowicz, A. Riddle, C. Qu, P. Calyam, and K. Palaniappan

Applied Imagery Pattern Recognition Workshop (AIPR), pgs. 1--12, 2020

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Unmanned Aerial Vehicle (UAV) systems with high-resolution video cameras are used for many operations such as aerial imaging, search and rescue, and precision agriculture. Multi-drone systems operating in Flying Ad Hoc Networks (FANETS) are inherently insecure and require efficient security schemes to defend against cyber-attacks such as e.g., Man-in-the-middle, Replay and Denial of Service attacks. In this paper, we propose a cloud-based, end-to-end security framework viz., "DroneNet-Sec" that provides secure network-edge connectivity, and computation security for drone video analytics to defend against common attack vectors in UAV systems. The DroneNet-Sec features a dynamic security scheme that uses machine learning to detect anomaly events and adopts countermeasures for computation security of containerized video analytics tasks. The security scheme comprises of a custom secure packet designed with MAVLink protocol for ensuring data privacy and integrity, without high degradation of the performance in a real-time FANET deployment. We evaluate DroneNet-Sec in a hybrid testbed that synergies simulation and emulation via an open-source network simulator (NS-3) and a research platform for mobile wireless networks (POWDER). Our performance evaluation experiments in our holistic hybrid-testbed show that DroneNet-Sec successfully detects learned anomaly events and effectively protects containerized tasks execution as well as communication in drones video analytics in a light-weight manner.