SNNAuth: Sensor-Based Continuous Authentication on Smartphones Using Spiking Neural Networks

Sensor-based continuous authentication mechanisms have demonstrated promising capabilities in enhancing the security of smart devices. In this article, we present SNNAuth, a novel sensor-based continuous Authentication system on smartphones that utilizes Spiking Neural Networks, leveraging biometric behavioral patterns captured by smartphone sensors. To enhance discriminative feature extraction, we introduce positional encoding into the time slicing of normalized sensor data. We design the artificial neural network (ANN)-SNN model, which transforms the trained ANN into an SNN by converting weights and activations into suitable spike neuron models and synaptic connections. The ANN-SNN model, designed for efficient computation and increased robustness, is specifically trained to extract temporal features of a legitimate user. With the extracted features of a legitimate user, we then train the one-class k-nearest neighbors (OC-kNN), which is employed for conducting the classification for all users. Based on the trained ANN-SNN and one-class kNN, SNNAuth determines whether the current user is legitimate or an imposter. Finally, we evaluate the performance of SNNAuth on two public data sets and our data set, and the experimental results demonstrate that SNNAuth outperforms state-of-the-art solutions by achieving the highest accuracy and the lowest equal error rates (EERs) on all three data sets.