Pathology

Operator (biology)

Computer network

Testbed

Correctness

Transcription factor

Gene

Algorithm

Distributed computing

World Wide Web

Engineering

Repressor

Systems engineering

Geolocation

Chemistry

Alternative medicine

Protocol (science)

Computer science

Adversary

Task (project management)

Real-time computing

Computer security

Biochemistry

Medicine

In this work, we synthesize collaboration protocols for human-unmanned aerial vehicle (H-UAV) command and control systems, where the human operator aids in securing the UAV by intermittently performing geolocation tasks to confirm its reported location. We first present a stochastic game-based model for the system that accounts for both the operator and an adversary capable of launching stealthy false-data injection attacks, causing the UAV to deviate from its path. We also describe a synthesis challenge due to the UAV's hidden-information constraint. Next, we perform human experiments using a developed RESCHU-SA testbed to recognize the geolocation strategies that operators adopt. Furthermore, we deploy machine learning techniques on the collected experimental data to predict the correctness of a geolocation task at a given location based on its geographical features. By representing the model as a delayed-action game and formalizing the system objectives, we utilize off-the-shelf model checkers to synthesize protocols for the human-UAV coalition that satisfy these objectives. Finally, we demonstrate the usefulness of the H-UAV protocol synthesis through a case study where the protocols are experimentally analyzed and further evaluated by human operators.

Security-Aware Synthesis of Human-UAV Protocols