Abstract
When authenticating illegal users, the existing account/password matching mechanism is only used to validate passwords. Therefore, it can not identify the web behavior of malicious users, such as identity fraud. In the case of fingerprint and face recognition mechanisms, which are prone to fraud, these mechanisms are still ineffective in preventing identity fraud. Recent years, behavioral analysis has been widely studied in view of the fraud-resistant characteristics of user behavior data and the personalized information contained therein. Among them, mobile terminal behavior is one of the main types of web user behavior. Aiming at these mobile terminal behaviors, a method of subspace dimension reduction is proposed to transform the multi-variable real-valued time series into univariable. It is realized by forming each sliding window of multivariate time series into a matrix. By this it generates a matrix sequence. Then we compare the angle difference between two adjacent matrices and transform the matrix sequence into a univariate time series, and then we propose the Gaussian-processes based authentication method to verify a user’s web behavior. The whole process is called Subspace-based dimension Reduction Gaussian-process Authentication (SRGA) method. Finally, the effectiveness of proposed method is verified by a series of experiments.
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