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

PlainText, Bibtex, PDF, Google Scholar

Abstract

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.