A PUF and Fuzzy Extractor-Based UAV-Ground Station and UAV-UAV Authentication Mechanism With Intelligent Adaptation of Secure Sessions

It is crucial that communication between an unmanned aerial vehicle (UAV) and the ground station (GS) be secure, and both devices should mutually authenticate each other to ensure that an adversary cannot obtain communicated information. Moreover, dynamically adapting the session time of an authenticated session can decrease the idle time of a session and consequently reduce the window of opportunity for an adversary to interfere with the communication link. In light of these considerations, we design a physically unclonable function (PUF) and fuzzy extractor-based UAV-GS authentication mechanism called UAV Authentication with Adaptive Session (UAAS). In UAAS, both UAV-GS and UAV-UAV authentication are two-way. We use a Thompson Sampling (TS)-based approach to intelligently adapt the duration of a session. Both formal and informal security proofs are presented along with a computation and communication cost analysis to analyze the performance of UAAS. It is noted that UAAS is secure against various well-known attacks and has a lower communication cost than several baseline mechanisms. Due to the noise reduction that occurs in PUFs, the computational cost of UAAS is higher than that of baselines that ignore noise. Also, our simulation shows that UAAS significantly outperforms baselines when it comes to network performance.