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.

翻译：协作感知通过融合外部数据显著增强网联自动驾驶车辆的感知能力，但同时也引入了潜在安全风险。网联自动驾驶车辆的驾驶决策依赖于外部不可信数据，使其容易遭受协作感知系统中恶意参与者发起的攻击。然而，目前针对此类威胁的安全分析与防御机制尚属空白。为探究该脆弱性的影响，我们首次提出多种实时数据伪造攻击方法，攻击者向目标车辆发送精心构造的恶意数据以干扰其感知结果，进而导致急刹车或碰撞风险增加。我们的攻击在高保真模拟场景中实现了超过86%的成功率，并在真实场景实验中得以复现。针对该脆弱性，我们提出系统性异常检测方法，使良性车辆能够协同揭露恶意数据伪造行为。该方法在模拟场景中实现了91.5%的检测率及3%的误报率，并在真实场景中显著降低了攻击影响。