Analysis method of visual fusion of network security data

In this paper a visual fusion analysis method for network security data based on time series is proposed. The correlation between network data is used for feature selection and consistency filtering. The original features are preserved while the redundant features are eliminated. The information entropy between network data features is proposed. Calculation, using the INTERACT method to achieve normalization processing; through the time series analysis method for visual fusion analysis of the processed network data, using the Chebyshev method to verify the network packets to determine whether there is intrusion behavior, to achieve network security data Visual fusion. The experimental results show that the detection rate can be improved by regulating the detection threshold. With the continuous expansion of data size, the detection rate of the proposed method is relatively stable, and the false alarm rate is lower than other methods.