In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

2026-06-26

AI Summary

This study addresses the vulnerability of connected vehicle communications to Sybil attacks, which compromise driving safety and the reliability of collision warnings. The authors propose the first vehicular digital twin framework that integrates a Temporal Convolutional Network (TCN) to model temporal trajectory features and employs the Hierarchical Navigable Small World (HNSW) algorithm for efficient similarity-based classification. This unified approach simultaneously achieves high-precision collision warning and Sybil attack detection. Experimental results demonstrate that, while meeting automotive-grade real-time requirements, the method attains a Sybil attack detection accuracy of 0.984 and a recall of 1.00. Furthermore, it reduces the Time-to-Event Threshold (TET) and Time-to-Impact Threshold (TIT) metrics for near-collision events by 88% and 72%, respectively, substantially enhancing system safety and early-warning efficacy.

Abstract

Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety. These communication channels can be exploited by adversaries to launch cyberattacks such as Sybil attacks, which could threaten both safety-critical and mobility applications, leaving CVs vulnerable and putting human lives at risk. As CV deployment continues to expand, the need to detect and mitigate cyberattacks in real-time becomes increasingly urgent. This study presents an in-vehicle Digital Twin (DT)-based collision warning framework with built-in capabilities for Sybil attacks detection. The framework integrates a Temporal Convolutional Network (TCN) for learning temporal dependencies in vehicle trajectory data and Hierarchical Navigable Small World (HNSW) algorithms for efficient similarity-based classification. Our framework is evaluated on real-world Sybil attack data, collected through field experiments. The framework achieved accuracy, recall, and F1 scores of 0.984, 1.00, and 0.944, respectively, in detecting Sybil-generated fake vehicles. During the safety evaluation, the framework reduced the mean Time Exposed Time-To-Collision (TET) and mean Time Integrated Time-To-Collision (TIT) of near-collision events by 88% and 72%, respectively. Furthermore, real-world feasibility evaluation shows that the framework conformed to the standardized maximum allowable latency for safety applications and operated well within the capacity of modern processors -- demonstrating the promise of an in-vehicle DT-based framework as an attack mitigation mechanism against Sybil attacks for next-generation CVs.

Problem

• Connected Vehicles
• Sybil Attack
• Collision Warning
• Cybersecurity
• Digital Twin

Innovation

• Digital Twin
• Sybil Attack Detection
• Temporal Convolutional Network
• HNSW
• Connected Vehicles