As drones are increasingly used in various civilian applications, the security of drone communications is a growing concern. In this context, we propose novel strategies for challengeresponse physical layer authentication (CR-PLA) of drone messages. The ground receiver (verifier) requests the drone to move to a defined position (challenge), and authenticity is verified by checking whether the corresponding measured channel gain (response) matches the expected statistic. In particular, the challenge is derived from a mixed strategy obtained by solving a zero-sum game against the intruder, which in turn decides its own positions. In addition, we derive the optimal strategy for multiround authentication, where the CR-PLA procedure is iterated over several rounds. We also consider the energy minimization problem, where legitimate users want to minimize the energy consumption without compromising the security performance of the protocol. The performance of the proposed scheme is tested in terms of both security and energy consumption through numerical simulations, considering different protocol parameters, different scenarios (urban and rural), different drone altitudes, and also in the context of drone swarms.

The identification of the devices from which a message is received is part of security mechanisms to ensure authentication in wireless communications. Conventional authentication approaches are cryptography-based, which, however, are usually computationally expensive and not adequate in the Internet of Things (IoT), where devices tend to be low-cost and with limited resources. This paper provides a comprehensive survey of physical layer-based device fingerprinting, which is an emerging device authentication for wireless security. In particular, this article focuses on hardware impairment-based identity authentication and channel features-based authentication. They are passive techniques that are readily applicable to legacy IoT devices. Their intrinsic hardware and channel features, algorithm design methodologies, application scenarios, and key research questions are extensively reviewed here. The remaining research challenges are discussed, and future work is suggested that can further enhance the physical layer-based device fingerprinting.