A Game of Drones: Cyber-Physical Security of Time-Critical UAV Applications with Cumulative Prospect Theory Perceptions and Valuations

Abstract

The effective deployment of unmanned aerial vehicle (UAV) systems and services hinges on securing them against cyberphysical attacks that can jeopardize their mission or use them to intrude into secured perimeters. In this paper, a novel mathematical framework is introduced for modeling and analyzing the cyberphysical security of time-critical UAV applications. A general UAV security network interdiction game is formulated to model interactions between a UAV operator and an interdictor, each of which can be benign or malicious. In this game, the interdictor chooses the optimal location(s) from which to target the drone system by interdicting the potential paths of the UAVs. Meanwhile, the UAV operator responds by finding an optimal path selection policy that enables its UAVs to evade attacks and minimize their mission completion time. New notions from cumulative prospect theory (PT) are incorporated into the game to capture the operator's and interdictor's subjective valuations of mission completion times and perceptions of the risk levels facing the UAVs. The equilibrium of the game, with and without PT, is then analytically characterized and studied. Novel algorithms are then proposed to reach the game's equilibria under both PT and classical game theory. Simulation results show the properties of the equilibrium for both the rational and PT cases. The results show that the operator's and interdictor's bounded rationality is more likely to be disadvantageous to the UAV operator. In particular, the more distorted the perceptions and valuations of the operator are, the higher is its achieved expected mission completion time.

Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

“…Thus, the optimal imposed location by the attacker, in this case, is the intersection of circle C and a circle C with a radius of e 2 max + d 2 i (t) with its center at the actual desired destination of the UAV. Circle C can be represented formally as in (13). Note that the two circles will always intersect in two points, however, only one of them will minimize the objective function and, hence, this point will also be a solution for the maximization problem in the first constraint.…”

“…Examples include cyber attacks such as, false data injection [12] and physical attacks such as targeting the UAVs using firearms or hunting rifles. Cyber-physical attacks, on the other hand, represent a category of sophisticated attacks that aim at causing both cyber and physical damage to the UAV such as GPS spoofing, GPS jamming, hijacking the connection between a UAV and its controller, and thwarting delivery drones [13].…”

On greedy and strategic evaders in sequential interdiction settings with incomplete information

“…As mentioned earlier, the evader's perspective is discussed by relatively few studies. In particular, Sanjab et al [30,31] study a one-stage zero-sum game, where the evader transports products between two specified nodes in the network and attempts to minimize the expected delivery time. Furthermore, the authors focus on a bounded rationality model, that is, the risk level of a selected path or the merit of a selected attack location can be perceived subjectively by the decision-makers.…”

“…However, in our setting the bounded rationality of the evader is addressed by considering strategic decisions in a multi-period deterministic setting. Additionally, in contrast to [30,31] we assume limited information about the network's structure, but not uncertainty in the evader's (or interdictor's) decisions.…”