Game theory is being used in cybersecurity to observe different attacks as it can provide a mathematical representation of the interactions between system admins, hackers, and users. The game-theoretic solution determines the favorable parameters (strategies), predicts the player’s behavior, and suggests the best settings for minimizing the attack’s effect. To this end, our paper attempts to study the usefulness of game-theoretic applications for the prevention of Distributed Denial of Service (DDoS) attacks on a drone by deriving the information from conventional game solutions and augmenting that with the bounded rationality concept called Quantal response equilibrium (QRE). In this process, we identify feasible strategies for each player through simulations and formulate five non-cooperative game scenarios for two variants of DDoS attacks. In these games, the traditional game-theoretic solution or Nash Equilibrium (NashE) provides information about the drone’s recommended settings, the hacker’s preferred strategy, and the game-theoretic threshold assuming that all participants are highly intelligent. We augment this information by considering the participant’s tendency to make errors and the evolution in their behavioral pattern from zero to high-values of rationality using QRE. The information coupled from NashE and QRE provides better clarity to a drone operator, thus improving the drone’s security by two levels and allowing the drone operator to take timely precautions. Inspired by this multilevel process, we propose an equivalent real-world framework for protecting Unmanned Aerial Vehicle (UAV) nodes against a DDoS attack.