V-TRACE: Persistent Tracking Misbehaving Vehicles under Pseudonym Changes in V2X Networks
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As connected and cooperative intelligent transportation systems (C-ITS) advance with vehicle-to-everything (V2X) communication, the risk of cyber threats such as false data injection and message flooding continues to grow. We present V-TRACE, a lightweight and standard-compliant framework for vehicle tracking and identification that operates effectively even under frequent pseudonym changes. V-TRACE initiates real-time tracking upon detecting anomalous behavior and correlates spatiotemporal patterns across V2X messages to sustain traceability. When identifier rotation occurs, the system seamlessly transitions to an identification phase that maintains persistent monitoring and suppresses further impact. Unlike approaches that require protocol modifications, V-TRACE operates fully within existing V2X standards, ensuring compatibility with current C-ITS deployments. We implement and evaluate V-TRACE using a high-fidelity CANoe.Car2x simulation. Experimental results demonstrate that V-TRACE reliably tracks and re-identifies misbehaving vehicles with minimal overhead, confirming its scalability and practicality in real-world traffic scenarios.
Abstract
As connected and cooperative intelligent transportation systems (C-ITS) advance with vehicle-to-everything (V2X) communication, the risk of cyber threats such as false data injection and message flooding continues to grow. We present V-TRACE, a lightweight and standard-compliant framework for vehicle tracking and identification that operates effectively even under frequent pseudonym changes. V-TRACE initiates real-time tracking upon detecting anomalous behavior and correlates spatiotemporal patterns across V2X messages to sustain traceability. When identifier rotation occurs, the system seamlessly transitions to an identification phase that maintains persistent monitoring and suppresses further impact. Unlike approaches that require protocol modifications, V-TRACE operates fully within existing V2X standards, ensuring compatibility with current C-ITS deployments. We implement and evaluate V-TRACE using a high-fidelity CANoe.Car2x simulation. Experimental results demonstrate that V-TRACE reliably tracks and re-identifies misbehaving vehicles with minimal overhead, confirming its scalability and practicality in real-world traffic scenarios.