User security behavior baseline analysis method based on fusion machine learning algorithm

Abstract

The invention discloses a user security behavior baseline analysis method based on a fusion machine learning algorithm. The method comprises the following steps: establishing a behavior feature matrixfor a user or equipment to be monitored; establishing a two-dimensional feature matrix based on time dimension; positioning users or equipment with abnormal behavior baselines; extracting feature vectors of time dimensions from users or equipment with abnormal behaviors; and positioning the time when the baseline abnormality occurs. According to the invention, based on the fusion machine learningalgorithm, abnormal matching is carried out on the security of user behaviors from two aspects of behavior features and behavior occurrence frequency, so that whether the behaviors of the user threaten the network security or have the possibility of penetration attack is judged.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a baseline analysis method for user security behavior based on a fusion machine learning algorithm. Background technique [0002] With the development of cyberspace security and big data technology, the security analysis platform for user behavior analysis has entered a stage of rapid development. How to find abnormal behavior in a large amount of data has become the focus of research by various manufacturers and platforms. User behavior baseline anomaly analysis is an important part of user behavior analysis in many scenarios. [0003] User security behavior analysis is fundamentally different from ordinary user behavior analysis on e-commerce platforms. The baseline anomaly analysis of user behavior mostly uses artificial intelligence analysis methods, such as Bayesian, linear regression, etc. These algorithms require a large number of training samples, and these alg...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a user security behavior baseline analysis method based on a fusion machine learning algorithm, which is used to solve the problem that the traditional baseline abnormal analysis method of user behavior cannot accumulate a large number of training samples and carry out intelligent training of large data sets

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