Non-intrusive Reprogrammable Device Authentication Using Low-cost Motion Sensors in Wearables

The rise of wearables such as fitness trackers and smartwatches has increased the need for strong security to protect personal data. Although two-factor authentication methods improve security, they often require additional user input, making them inconvenient. Recently, hardware flaws in accelerometers and WiFi interfaces have been leveraged to create low-effort two-factor authentication methods. However, these hardware-based device credentials are static, necessitating device replacement if the credentials are compromised. In this study, we introduce an innovative device authentication system that identifies wearables using vibration-based credentials. By utilizing built-in vibration motors and motion sensors (i.e., accelerometers and gyroscopes), our system establishes a unique communication channel to capture the distinct characteristics of each device. Unlike existing methods, our vibration-based credentials are reprogrammable and user-friendly. We develop advanced data processing techniques to minimize the impact of noise, body motion artifacts, and wearing position. We design a lightweight convolutional neural network for feature extraction and device authentication, with a majority vote mechanism to improve identification robustness. Extensive experiments with five different smartwatches demonstrate that our system achieves an average precision of 98% and a recall of 94% under various attacks, demonstrating that including gyroscope data significantly improves performance across different wearing poses and watch orientations.