Turning Noises to Fingerprint-Free "Credentials": Secure and Usable Drone Authentication (2302.09197v2)

Abstract: Drones have been widely used in various services, such as delivery and surveillance. Authentication forms the foundation of the security of these services. However, drones are expensive and may carry important payloads. To avoid being captured by attackers, drones should keep a safe distance from the verifier before authentication succeeds. This makes authentication methods that only work in very close proximity not applicable. Our work leverages drone noises for authentication. While using sounds for authentication is highly usable, how to handle various attacks that manipulate sounds is an \emph{unresolved challenge}. It is also unclear how to ensure robustness under various environmental sounds. Being the first in the literature, we address the two major challenges by exploiting unique characteristics of drone noises. We thereby build an authentication system that does \emph{not} rely on any drone sound fingerprints, keeps resilient to attacks, and is robust under environmental sounds. An extensive evaluation demonstrates its security and usability.