Operator Strategy Model Development in UAV Hacking Detection

Abstract

An increasingly relevant security issue for unmanned aerial vehicles (UAVs, also known as drones) is the possibility of a global positioning system (GPS) spoofing attack. Given the existing problems in current GPS spoofing detection techniques and human visual advantages in searching and localizing targets, we propose a human-autonomy collaborative approach of human geo-location to assist UAV control systems in detecting GPS spoofing attacks. An interactive testbed and experiment were designed and used to evaluate this approach, which demonstrated that human-autonomy collaborative hacking detection is a viable concept. Using the hidden Markov model (HMM) approach, operator behavior patterns and strategies from the experiment were modeled via hidden states and transitions among them. These models revealed two dominant hacking detection strategies. Statistical results and expert performer evaluations show no significant difference between different hacking detection strategies in terms of correct detection. The detection strategy model suggests areas of future research in decision support tool design for UAV hacking detection. Also, the development of HMMs presents the feasibility of quantitatively investigating operator behavior patterns and strategies in human supervisory control scenarios.