论文标题
针对AV安全的ML驱动的恶意软件
ML-driven Malware that Targets AV Safety
论文作者
论文摘要
确保自动驾驶汽车（AV）的安全对于他们的大规模部署和公众采用至关重要。但是，违反安全限制和造成事故的安全攻击是实现公众对AVS的信任的重要威慑力，并阻碍了供应商部署AVS的能力。从攻击者的角度来看，造成安全危害，导致严重的安全妥协（例如，事故）令人信服。在本文中，我们介绍了一种攻击模型，一种以智能恶意软件形式部署攻击的方法，以及对其对生产级自动驾驶软件的影响的实验评估。我们发现，确定发动攻击的时间间隔对于造成安全危害（例如碰撞）很重要的时间很重要。例如，智能恶意软件造成的紧急制动比随机攻击造成了33倍，而驾驶模拟中有52.6％的事故。
Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption. However, security attacks that violate safety constraints and cause accidents are a significant deterrent to achieving public trust in AVs, and that hinders a vendor's ability to deploy AVs. Creating a security hazard that results in a severe safety compromise (for example, an accident) is compelling from an attacker's perspective. In this paper, we introduce an attack model, a method to deploy the attack in the form of smart malware, and an experimental evaluation of its impact on production-grade autonomous driving software. We find that determining the time interval during which to launch the attack is{ critically} important for causing safety hazards (such as collisions) with a high degree of success. For example, the smart malware caused 33X more forced emergency braking than random attacks did, and accidents in 52.6% of the driving simulations.
