Secure 5G Positioning with Truth Discovery, Attack Detection and Tracing

Abstract

The fifth-generation (5G) cellular network is expected to provide sub-meter positioning accuracy without draining the battery of user equipment. As a solution, ultra-dense network (UDN) deployment and network-based positioning were proposed. However, the openness of UDN and the vulnerability of network devices (e.g., access nodes) make it easy for attackers to poison such a positioning system. However, no existing work explores how to overcome this issue. This paper concentrates on jamming and collusion attacks in the network based positioning system. Specifically, we design a novel scheme that contains three functional modules to erase the influence of these attacks. A truth discovery module applies a clustering-based method aiming to generate the most approximate position value and find out suspicious signals. Based on neural network models, we further develop an attack detection module and an attack tracing module to perceive attacked user equipment and locate malicious or attacked access nodes. Through simulation, we conduct extensive experiments to illustrate the effectiveness of our scheme. The result shows high detection and tracing accuracy with very simple neural network models, which also implies the potential of our proposed scheme in practical deployment.