Benchmark for Non-reciprocal Attack Detection on Synchronization

TL;DR

The paper addresses the vulnerability of ultra-precise time synchronization to non-reciprocal attacks on physical links. It introduces the BAND framework, which models non-reciprocal delays as discrete effective attack points governed by a homogeneous Poisson process and two attack kernels (Type I and Type II), linking attack effects to white phase noise and white frequency noise through an attack intensity $I=\lambda \mathcal{T}_0^2$, with detection via changes in time deviation (TDEV). The authors validate the approach on a 100-km dual-comb two-way optical time transfer system, showing accurate prediction of instantaneous and accumulating attacks with discrepancies within $\sim$5%, and extend the framework to explain intrinsic fiber noise and delay-unsuppressed noise. This work provides a physically grounded, environment-sensitive framework for securing large-scale time, sensing, and communication networks by offering quantitative metrics to detect and mitigate non-reciprocal attacks on optical links.

Abstract

A precise and secure time synchronization is the backbone of both fundamental physics and advanced technologies. Despite ultra-high precision, security, particularly the unresolved vulnerabilities on physical links beyond traditional cryptography, remains the bottleneck. Here, we develop an analysis framework, the Benchmark Attack Noise Detection (BAND) model, to quantify the security of the high-precision time synchronization when its core assumption, reciprocity, is broken. The model characterizes non-reciprocal attacks through a unified metric, termed attack intensity. Based on the analysis of attack intensity, we accurately predict both instantaneous and accumulating attacks in a 100-km dual-comb two-way time-transfer system. Beyond security considerations, we find that the BAND model can explain intrinsic fiber noise and delay-unsuppressed noise naturally, offering an effective tool to establish an environment-sensitive link model. This work paves the way for a secure large-scale integrated time, sensing and communication networks.

Figures (6)

• Figure 1: High-precision time transfer between non-portable clocks relies on the Einstein synchronization protocol, which exchanges time information through periodical pulses of the same frequency between spatially separated clocks Alice and Bob. This protocol is founded on the assumption that the propagation delay within the synchronization channel is symmetric in both directions. However, this assumption is not always valid in real-world scenarios, as potential adversaries Mallory can deliberately introduce non-reciprocal time delays $\Delta\tau_{\text{NR}}$ to deceive Alice and Bob into accepting incorrect timing information $\left(\Delta T_{A\rightarrow B}-\Delta T_{B\rightarrow A}+\Delta\tau_{\text{NR}}\right)/2$, rather than $\left(\Delta T_{A\rightarrow B}-\Delta T_{B\rightarrow A}\right)/2$, without any awareness of the discrepancy.
• Figure 2: Instantaneous disturbance attack. (a) Attack behavior. (b) TDEV comparison: secure system (black), attacked system (orange), model prediction (blue), and estimated $10I_{\text{theo}}$ (purple).
• Figure 3: Accumulating delay attack. (a) Attack behavior. (b) TDEV comparison: secure system (black), attacked system (orange), model prediction (blue), and estimated $10I_{\text{theo}}$ (purple).
• Figure 4: One-way signal under different temperature conditions. (a) Phase time domain behavior. (b) TDEV of linear fast drift (red), linear slow drift (blue) and non-linear drift (yellow).
• Figure 5: Visualization of EAPs. EAPs originate from discretization of real attack due to limited measurement resolution.