MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars
arxiv(2023)
摘要
Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars
play a critical role in many of the advanced driver assistance systems (ADAS)
featured on today's vehicles. While previous works have demonstrated (only)
successful false-positive spoofing attacks against these sensors, all but one
assumed that an attacker had the runtime knowledge of the victim radar's
configuration. In this work, we introduce MadRadar, a general black-box radar
attack framework for automotive mmWave FMCW radars capable of estimating the
victim radar's configuration in real-time, and then executing an attack based
on the estimates. We evaluate the impact of such attacks maliciously
manipulating a victim radar's point cloud, and show the novel ability to
effectively `add' (i.e., false positive attacks), `remove' (i.e., false
negative attacks), or `move' (i.e., translation attacks) object detections from
a victim vehicle's scene. Finally, we experimentally demonstrate the
feasibility of our attacks on real-world case studies performed using a
real-time physical prototype on a software-defined radio platform.
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