Radio frequency (RF) fingerprinting is a hardware feature used in Internet of Things (IoT) applications to identify wireless devices. In this paper, we propose few-shot learning (FSL) and prototypical networks (PTNs) to create a new model that can adapt to a new domain with very few labeled examples. The proposed model can mitigate the domain shift caused by changing RF environments. Experimental results show the proposed method can improve the performance of RF fingerprinting over different domains.

A fundamental problem that confronts virtual private network (VPN) applications is the overhead on throughput, ease of deployment and use, and overall utilization. WireGuard is a recently introduced light and secure cross-platform VPN application. It aims to simplify the process of setting up a secure connection while utilizing the multi-threading capability and minimizing the use of bandwidth. There have been several follow-up studies on WireGuard since its birth, most of which focus on the security analysis of the protocol. Despite the author's claim that WireGuard has impressive wins over OpenVPN and IPsec, there is no rigorous analysis of its performance to date. This paper presents a performance comparison of WireGuard and its main rival OpenVPN on various metrics. We construct an automated test framework and deploy it on a total of eight nodes, including remote AWS instances and local virtual machines. Our test results clearly show two main edges that WireGuard has over OpenVPN, its performance on multi-core machines and its light codebase.