Analysis method for visual fusion of multi-source heterogeneous network security data
At present,the network security field is faced with various data sources and heterogeneous structures.Conventional network security data visualization fusion analysis methods are susceptible to large-scale dynamic changes in time,which leads to high cross-entropy difference of the fusion data.Therefore,a new method of multi-source heterogeneous network data visualization fusion analysis is proposed.The Apriori algorithm is used for visual association mining of multi-source heterogeneous network data,and the evaluation index system of multi-source heterogeneous network data is constructed to complete the visual fusion analysis of multi-source heterogeneous network security data.The experimental results show that the cross entropy of fusion data obtained by the designed data visualization fusion analysis method is consistent with the actual data cross entropy,indicating that the design method can effectively improve the analysis efficiency and accuracy of network security data,and provide strong support for network security monitoring and early warning.
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