SafeLead: Detecting and Excluding Random STS Attack in UWB Ranging System

Zhuo Ma, Jiayu Jin, Yang Liu, Yilong Yang, Xinjing Liu, Teng Li, Junwei Zhang, Jianfeng Ma

Abstract: Ultra-wideband (UWB) technology employs ex-tremely short pulses in wireless communication, which enhances signal transmission security and accuracy, making it ideal for spatial awareness. However, recent research indicates that, with random scrambling timestamp sequences (STS), attackers can confuse the leading edge detection for 802.1S.4z authentication, and then forge a shorter ranging result than the actual distance. While existing defenses focus on detection, follow-up countermeasures remain blank. Intuitively discarding signals containing intensive attacks may cause a sharply decrease in ranging frequency or further service interruptions inadvertently facilitating unauthorized access. To address this issue, we propose SafeLead, a novel leading edge detection algorithm designed to exclude attacks and revive legitimate leading edges from attacked signals. For attack detection, SafeLead introduces a new thresh-old based on the variance of leading edges to avoid uncertainties from environmental changes. Furthermore, SafeLead leverages correlations between sub-templates to exclude disordered leading edges and revive legitimate timestamps. Simulations demonstrate that SafeLead achieves optimal performance with over 90 % attack detection rate and 90 % ranging accuracy, representing an 87% accuracy improvement compared to state-of-the-art methods.

External IDs:dblp:conf/infocom/0001J00L00025