A security visualization monitoring method based on big data

Abstract

This invention relates to the field of data visualization technology, and more particularly to a security visualization monitoring method based on big data. The method includes the following steps: acquiring and classifying raw data streams from multiple business systems according to their source type to form a data partition set, wherein the data partition set contains several data partitions; subsequently, extracting the basic attributes of each data block to generate a data activity feature table, and constructing a data sensitivity visual mapping unit, converting feature weights into a security visual mapping graph; monitoring areas with abnormal intensity changes in the mapping graph to determine security risk areas; generating risk warning information based on the risk areas and outputting it to a security visualization monitoring terminal to achieve real-time risk identification and early warning. This invention achieves real-time visual perception, accurate identification, and dynamic control of security risks in a big data environment through multi-source data fusion modeling, sensitivity visual mapping, adaptive anomaly aggregation detection, and a risk feedback closed-loop mechanism.

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