RPWAEAuth: Sensor-Based Continuous Authentication Using Reconstruction Probability in Wasserstein Autoencoder

Nowadays, with the widespread adoption of mobile devices, information security has become particularly important. Existing sensor-based continuous authentication systems ensure the security of mobile devices to some extent, but most have drawbacks, such as lacking end-to-end structure or requiring data from both legitimate users and imposters for training. In this paper, we present RPWAEAuth, a sensor-based continuous Authentication system using Reconstruction Probability in the Wasserstein AutoEncoder. RPWAEAuth implicitly collects user behavior patterns from the built-in accelerometer, gyroscope, and magnetometer of mobile devices. The Wasserstein autoencoder maps the sensor data into a continuous latent space close to a prior distribution and reconstructs them using reconstruction probability for better authentication. In the registration stage, RPWAEAuth collects and preprocesses the sensor data from a legitimate user for RPWAE training. In the authentication stage, when a user interacts with the device, RPWAEAuth collects and preprocesses the sensor data, and then feeds them into the trained RPWAE to generate a reconstruction probability. This probability is then compared with a predefined threshold for user authentication. We evaluate the performance of RPWAEAuth on our dataset in terms of the effectiveness of RPWAEAuth, impact of sensor numbers, effectiveness of reconstruction probability, authentication time, resilience to mimic attacks, comparison with different AEs, and comparison with state-of-the-art methods. The experimental results demonstrate that RPWAEAuth achieves superior authentication performance compared to other methods, with an accuracy of 99.34% and an EER of 0.66% on 69 unseen users.