Gyro-Mag: Attack-Resilient System Based on Sensor Estimation.

Several researchers recently demonstrated that attackers can interfere with an inertial measurement unit (IMU) sensor's normal function or take complete control of sensor measurements by physically injecting malicious signals into the sensor. Although there are existing methods for detecting such signal injection attacks, most do not provide resilience. Indeed, detection-only methods cannot respond when attacks have already occurred, which results in accidents such as crashes or falls. In this paper, we propose the first method that can detect signal injection attacks on IMU sensors based on the relation between the gyroscope and the magnetometer, and provide long-term resilience against these attacks. We construct a mathematical model to estimate one sensor's data from the other's data based on their relation. With this mathematical model, the device can detect signal injection attacks on the IMU sensor and continue to function in a near-normal state based on the estimated data. Our method can be easily adapted to deployed devices since it requires only estimation software and no additional hardware. We evaluated our method using a total of five open datasets and commercial devices. Our method has a resilience of 99.78% against signal injection attacks while consuming only reasonable computational costs.