A machine learning-based network security posture prediction method, device and medium

Abstract

The application discloses a network security situation prediction method and device based on machine learning and a medium, relates to the technical field of network security, and comprises the following steps: acquiring multi-modal network security data, and constructing a multi-scale time sequence feature tensor; performing weight adjustment based on threat feature importance, and generating a threat reinforced feature tensor; performing self-adaptive feature selection and time sequence correlation analysis on the threat reinforced feature tensor, and constructing a threat correlation feature tensor; performing feature-entity mapping and situation evolution modeling in combination with the threat correlation feature tensor and a network entity identifier, and generating an entity situation feature tensor; inputting the entity situation feature tensor into a deep neural network, and generating a situation representation vector sequence through multi-layer nonlinear transformation; and performing multi-step situation deduction and threat propagation path analysis based on the situation representation vector sequence, and outputting a network security situation prediction result. Through multi-scale time sequence feature construction and self-adaptive weight adjustment based on threat importance, the application improves the accuracy of situation prediction.

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