CNNAuth: Continuous Authentication via Two-Stream Convolutional Neural Networks

Abstract

We present a two-stream convolutional neural network based authentication system, CNNAuth, for continuously monitoring users' behavioral patterns, by leveraging the accelerometer and gyroscope on smartphones. We are among the first to exploit two streams of the time-domain data and frequency-domain data from raw sensor data for learning and extracting universal effective and efficient feature representations as the inputs of the convolutional neural network (CNN), and the extracted features are further selected by the principal component analysis (PCA). With these features, we use the one-class support vector machine (SVM) to train the classifier in the enrollment phase, and with the trained classifier and testing features, CNNAuth classifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the performance of the two-stream CNN and CNNAuth, respectively, and the experimental results show that the two-stream CNN achieves an accuracy of 87.14%, and CNNAuth reaches the lowest authentication EER of 2.3% and consumes approximately 3 seconds for authentication.