SCANet

Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this paper, we present SCANet, a two-stream convolutional neural network based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data frequency domain data and temporal difference domain data from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features, and then performs the principal component analysis (PCA) to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine (one-class SVM) to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate (EER) on two datasets and takes approximately 3 seconds for user authentication.