An insider user authentication method based on improved temporal convolutional network

With the rapid development of information technology, information system security and insider threat detection have become important topics for organizational management. In the current network environment, user behavioral bio-data presents the characteristics of nonlinearity and temporal sequence. Most of the existing research on authentication based on user behavioral biometrics adopts the method of manual feature extraction. They do not adequately capture the nonlinear and time-sequential dependencies of behavioral bio-data, and also do not adequately reflect the personalized usage characteristics of users, leading to bottlenecks in the performance of the authentication algorithm. In order to solve the above problems, this paper proposes a Temporal Convolutional Network method based on an Efficient Channel Attention mechanism (ECA-TCN) to extract user mouse dynamics features and constructs an one-class Support Vector Machine (OCSVM) for each user for authentication. Experimental results show that compared with four existing deep learning algorithms, the method retains more adequate key information and improves the classification performance of the neural network. In the final authentication, the Area Under the Curve (AUC) can reach 96%.