On Data Fabrication in Collaborative Vehicular Perception: Attacks and Countermeasures
This addresses a critical safety problem for connected and autonomous vehicles by mitigating attacks that could cause collisions, though it is incremental as it builds on existing collaborative perception systems.
The paper tackles the security risks in collaborative vehicular perception by proposing data fabrication attacks that achieve over 86% success rate in simulations and real-world tests, and it presents a countermeasure that detects 91.5% of attacks with a 3% false positive rate.
Collaborative perception, which greatly enhances the sensing capability of connected and autonomous vehicles (CAVs) by incorporating data from external resources, also brings forth potential security risks. CAVs' driving decisions rely on remote untrusted data, making them susceptible to attacks carried out by malicious participants in the collaborative perception system. However, security analysis and countermeasures for such threats are absent. To understand the impact of the vulnerability, we break the ground by proposing various real-time data fabrication attacks in which the attacker delivers crafted malicious data to victims in order to perturb their perception results, leading to hard brakes or increased collision risks. Our attacks demonstrate a high success rate of over 86% on high-fidelity simulated scenarios and are realizable in real-world experiments. To mitigate the vulnerability, we present a systematic anomaly detection approach that enables benign vehicles to jointly reveal malicious fabrication. It detects 91.5% of attacks with a false positive rate of 3% in simulated scenarios and significantly mitigates attack impacts in real-world scenarios.