SoundFence: Securing Ultrasonic Sensors in Vehicles Using Physical-Layer Defense

TL;DR

This paper introduces SoundFence, a physical-layer defense system for ultrasonic sensors in autonomous vehicles, effectively detecting signal injection attacks with high accuracy without extra hardware.

Contribution

The paper presents a novel physical-layer defense mechanism for ultrasonic sensors that detects malicious signal injections using existing sensor processing capabilities.

Findings

Detects over 95% of abnormal ultrasonic sensor readings

Accurately distinguishes real echoes from injected signals

Operates without additional hardware or equipment

Abstract

Autonomous vehicles (AVs), equipped with numerous sensors such as camera, LiDAR, radar, and ultrasonic sensor, are revolutionizing the transportation industry. These sensors are expected to sense reliable information from a physical environment, facilitating the critical decision-making process of the AVs. Ultrasonic sensors, which detect obstacles in a short distance, play an important role in assisted parking and blind spot detection events. However, due to their weak security level, ultrasonic sensors are particularly vulnerable to signal injection attacks, when the attackers inject malicious acoustic signals to create fake obstacles and intentionally mislead the vehicles to make wrong decisions with disastrous aftermath. In this paper, we systematically analyze the attack model of signal injection attacks toward moving vehicles. By considering the potential threats, we propose SoundFence, a physical-layer defense system which leverages the sensors' signal processing capability without requiring any additional equipment. SoundFence verifies the benign measurement results and detects signal injection attacks by analyzing sensor readings and the physical-layer signatures of ultrasonic signals. Our experiment with commercial sensors shows that SoundFence detects most (more than 95%) of the abnormal sensor readings with very few false alarms, and it can also accurately distinguish the real echo from injected signals to identify injection attacks.