Automatic loophole classification method based on word frequency statistics and simple Bayesian fusion model

Abstract

The invention relates to an automatic loophole classification method based on word frequency statistics and a simple Bayesian fusion model, and belongs to the technical field of information security. The method specifically comprises the steps of S1, building a loophole database, and collecting loophole records; S2, determining the class of a privilege set; S3, training a word frequency-reverse file frequency classifier; S4, testing a classification result and an accuracy rate of the word frequency-reverse file frequency classifier; S5, building a simple Bayesian classifier; S6, testing the classification result and the accuracy rate of the simple Bayesian classifier; And S6, fusing the classifiers. Compared with the existing methods, the automatic loophole classification method supporting loophole association mining provided by the invention has the advantage that a loophole description field in the loophole database is used, meanwhile, influence of loophole attributes such as availability scoring and influence scoring on loophole association are simultaneously considered, and classification accuracy is greatly improved.

Description

technical field [0001] The invention relates to a vulnerability automatic classification method based on word frequency statistics and a naive Bayesian fusion model, belonging to the technical field of information security. Background technique [0002] Network security vulnerability refers to the existence of some security holes in the computer system that may be exploited by malicious subjects (attackers or attack programs), which may lead to unauthorized access to system resources or other damage to the system. In recent years, the vulnerability scanning technology and the CVE (Common Vulnerabilities & Exposures, Common Vulnerabilities & Exposures, Common Vulnerabilities & Exposures) standard and the Common Vulnerability Scoring System (Common Vulnerability Scoring System, CVSS) vulnerability rating method that have gradually developed in recent years can detect and discover the vulnerabilities existing in the network, However, it is impossible to analyze the correlation ...

AI Technical Summary

Problems solved by technology

But its disadvantage is that it only uses the single attribute of the "vulnerability description" field in the database, and ignores the impact of other attributes of the vulnerability on the relevance of the vulnerability, such as the "availability score" and "impact score" of the vulnerability.

Therefore, the classification accuracy of this method still needs to be improved.

Images