A Game of Drones: Cyber-Physical Security of Time-Critical UAV Applications with Cumulative Prospect Theory Perceptions and Valuations
Not on the map. Why?
Abstract (arXiv)
In this paper, a novel mathematical framework is introduced for modeling and analyzing the cyber-physical 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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 1; 2025: 2.
4 papers.
- 2024Automated Security Response through Online Learning with Adaptive Conjecturescited by 13
- 2025UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVscited by 13
- 2021ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Preventioncited by 5
- 2025Human-AI Collaboration in Cloud Security: Cognitive Hierarchy-Driven Deep Reinforcement Learningcited by 1
- 2025UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs8 Jun 2025
- 2025Human-AI Collaboration in Cloud Security: Cognitive Hierarchy-Driven Deep Reinforcement Learning22 Feb 2025
- 2024Automated Security Response through Online Learning with Adaptive Conjectures19 Feb 2024
- 2021ADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention13 Jun 2021