FuMeAuth: Sensor-Based Continuous Authentication Using Fused Memory-Augmented Transformer Autoencoder

With the continual advancement of communication technologies, mobile devices have become indispensable tools in our daily lives. While existing sensor-based continuous authentication systems provide some level of user privacy protection, they often neglect the temporal characteristics of multi-sensor data and the unique information of each sensor. To further protect the privacy of mobile devices, we present FuMeAuth, a sensor-based continuous Authentication system using a Fused Memory-Augmented transformer Autoencoder. FuMeAuth leverages the built-in accelerometer, gyroscope, and magnetometer of smartphones to implicitly gather user behavior patterns. The Fused global-local Memory network (FuMe) effectively captures and adaptively combines the shared-private features of sensor data in FuMeAuth. During the registration phase, FuMeAuth collects and preprocesses the sensor data and sends the processed data to FuMe, which then records fused shared and private representations across different sensors for legitimate users. In the authentication phase, the trained FuMe reconstructs the current user’s data, computes the reconstruction error between user input data and the corresponding reconstructed data, and compares it against a predefined authentication threshold for authentication. We evaluate the performance of FuMeAuth on our dataset in terms of the effectiveness of FuMeAuth, effect of sensor numbers, efficiency of fused memory module, and comparison with state-of-the-art approaches. The experimental results demonstrate that FuMeAuth exhibits superior performance than other approaches by achieving an accuracy of 99.84% and an EER of 0.14% with 69 unseen users.