Abstract: Due to the large scale and variety of information data in the process of internet information security warning, the warning accuracy is low and the time is long. To improve the efficiency of early warning, a security warning for unknown access sources in mobile network privacy information databases is proposed. Principal component analysis method is used to reduce the dimensionality of information base data to reduce the difficulty of detection. The IMAP( Iterative Multivariate AutoRegressive Modelling and Prediction) algorithm is used to carry out data clustering processing, to extract discrete isolated data points, and complete the screening of unknown access source data in the information base. Unknown access source data is inputted into a support vector machine, a time window is used to transform the construction problem of the information base security warning model into a convex optimization problem of support vector machine learning. Security warning results are outputted, and globally optimize the construction parameters of the warning model are optimized to improve the warning output ability of the security warning model. The experimental results show that the proposed method has high security detection efficiency for information databases, and can achieve stable and accurate warning output in the face of multiple types of information database intrusion attacks.

Key words: principal component analysis method, iterative multivariate autoregressive modelling and prediction ( IMAP ) clustering method, time window, support vector machine learning method, convex optimization problem