Despite the widespread deployment of 5G technologies, there exists a critical gap in security testing for 5G Standalone (SA) devices. Existing methods, largely manual and labor-intensive, are ill-equipped to fully uncover the state of security in the implementations of 5G SA protocols and standards on devices, severely limiting the ability to conduct comprehensive evaluations. To address this issue, in this work, we introduce a novel, open-source framework that automates the security testing process for 5G SA devices. By leveraging enhanced functionalities of 5G SA core and Radio Access Network (RAN) software, our framework offers a streamlined approach to generating, executing, and evaluating test cases, specifically focusing on the Non-Access Stratum layer. Our application of this framework across multiple 5G SA devices provides in-depth security insights, significantly improving testing efficiency and breadth.

Security flaws and vulnerabilities in cellular networks lead to severe security threats given the data-plane services that are involved, from calls to messaging and Internet access. While the 5G Standalone (SA) system is currently being deployed worldwide, practical security testing of User Equipment (UE) has only been conducted and reported publicly for 4G/LTE and earlier network generations. In this paper, we develop and present the first open-source based security testing framework for 5G SA User Equipment. To that end, we modify the functionality of open-source suites (Open5GS and srsRAN) and develop a broad set of test cases for the 5G NAS and RRC layers. We apply our testing framework in a proof-of-concept manner to 5G SA mobile phones and provide detailed insights from our experiments. While being a framework in development, the results of our experiments presented in this paper can assist other researchers in the field and have the potential to improve 5G SA security.

Radio Frequency (RF) fingerprinting-based localization has become a research interest due to its minimum hardware requirement and satisfiable positioning accuracy. However, despite the significant attention this topic has gained, most of the research focused on the calculation of position estimates. In this paper, we propose a simple and novel method that can be used as an indicator of fingerprinting positioning error. The method is based on cluster radius evaluation of multiple fingerprinting data during the test phase, which can be used by a Location Based Service (LBS) provider to predict the user position estimation accuracy. This method can be used effectively in real-time to predict the estimation error and thereby assists the LBS to offer a better quality of service. The cluster radius also reveals the quality of the recorded radio map.