Location-aware Inaudible Attack Defense Towards Smart Speakers

Abstract

Recent studies show that inaudible attacks pose a non-negligible security risk to smart speakers. While several countermeasures have been proposed to detect the occurrence of the inaudible attack passively, accurately locating the attack source in 3D free space remains an unresolved challenge. Arrow  is designed to bridge this gap by attempting to detect the occurrence of inaudible attacks and determine their localization simultaneously. Instead of relying on dedicated hardware components, Arrow  is implemented with the microphone array widely deployed on COTS(Commercial Off-The-Shelf) smart speakers. Throughout the spatial information captured by the microphone array, Arrow  establishes a spatial mapping model and derives orientation-related features to pinpoint the location of the attack source. Furthermore, to improve the robustness against co-channel interference, Arrow  adopt carefully-modulated ultrasonic waveforms to achieve noise-robust attack detection. Through the above technical mechanism, Arrow  can significantly improve the security level of voice assistants on smart speakers with nearly zero deployment cost. We implement a prototype of Arrow and conduct comprehensive performance evaluation. The results show Arrow can achieve 2.5° and 7° error in DoA estimation for horizontal and vertical angles, respectively.