Stealthy Tracking of Autonomous Vehicles with Cache Side Channels
Stealthy Tracking of Autonomous Vehicles with Cache Side Channels
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DOI：
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发表时间：
2020
影响因子：
6.8
通讯作者：
Mulong Luo;A. Myers;G. Suh
中科院分区：
文献类型：
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作者：
Mulong Luo;A. Myers;G. Suh
自动驾驶汽车越来越受欢迎，但是在本文中，它们在物理世界中感知和操作的计算机系统的浮雕在本文中引入了新的安全风险。本地化软件尤其与攻击程序共享硬件平台，我们证明可以使用缓存侧通道攻击来推断路线或位置运行自适应蒙特卡洛定位（AMCL）算法的车辆的主要贡献。车辆或其环境。第二，我们引入了统计学习模型，从缓存访问模式推断出AMCL算法的状态第三个轨迹的车辆位置。位置隐私。
Autonomous vehicles are becoming increasingly popular, but their reliance on computer systems to sense and operate in the physical world introduces new security risks. In this paper, we show that the location privacy of an autonomous vehicle may be compromised by software side-channel attacks if localization software shares a hardware platform with an attack program. In particular, we demonstrate that a cache side-channel attack can be used to infer the route or the location of a vehicle that runs the adaptive Monte-Carlo localization (AMCL) algorithm. The main contributions of the paper are as follows. First, we show that adaptive behaviors of perception and control algorithms may introduce new sidechannel vulnerabilities that reveal the physical properties of a vehicle or its environment. Second, we introduce statistical learning models that infer the AMCL algorithm’s state from cache access patterns and predict the route or the location of a vehicle from the trace of the AMCL state. Third, we implement and demonstrate the attack on a realistic software stack using real-world sensor data recorded on city roads. Our findings suggest that autonomous driving software needs strong timing-channel protection for location privacy.