Unsupervised Sensor-Based Continuous Authentication With Low-Rank Transformer Using Learning-to-Rank Algorithms

With the rapid development of the Internet of Things (IoTs) and mobile communications, mobile devices have become indispensable in our daily lives. Given the substantial amount of private information stored on these devices, the security of mobile devices has emerged as a significant concern for users. Different from conventional methods such as PINs, fingerprints, and face IDs, which authenticate users only during the initial login stage, continuous authentication ensures consistent verification while mobile devices are in use. Current continuous authentication methods require extensive data from a series of users for effective training. Nevertheless, it is challenging to collect sufficient amount of data within a limited time. In this paper, we propose CALL, an unsupervised sensor-based Continuous Authentication system with a Low-rank transformer using Learning-to-rank algorithms. The lightweight CALL is capable of providing both spatial and temporal features for end-to-end authentication. Specifically, CALL utilizes time series data from a legitimate user, collected by the accelerometer, gyroscope, and magnetometer sensors on smartphones, to train a pure one-dimensional autoencoder for spatial features and a shuffle low-rank Transformer (SLRT) for temporal features in the training phase. In the authentication phase, the trained pure one-dimensional autoencoder captures spatial features by reconstructing input data to obtain the reconstruction error, and SLRT captures temporal features by predicting a ranking vector that reveals the order of the shuffled feature sequence. The predicted ranking vector is then used to recover the shuffled sequence and the similarity between the frequency spectrum sequences of the recovered sequence and the original time series data is calculated. The reconstruction error and similarity are compared against pre-defined thresholds, and CALL authenticates a user as legitimate only if both values fall below their respective thresholds. Finally, we evaluate the performance of CALL on UCI_HAR, WISDM_HARB, and our dataset, and the extensive experiments illustrate that CALL reaches the best performance with 96.43%, 95.24% and 96.92% accuracy, and 4.28%, 4.76% and 3.86% EERs on the three datasets, outperforming state-of-the-art continuous authentication methods.