Hierarchical clustering-based log audit method

Abstract

The invention discloses a hierarchical clustering-based log audit method. A system reads log information; a keyword in the log information is analyzed according to the read log information; the log information is subjected to word vector transformation according to the obtained keyword; and the system performs cluster analysis on obtained word vectors and displays small abnormal information obtained by analysis. According to the method, heavy work of manual log audit is avoided and abnormal log information can be automatically filtered, so that the user experience effect is enhanced.

Description

technical field [0001] The invention relates to the field of network security, in particular to a log audit method based on hierarchical clustering. Background technique [0002] With the development of informatization, network security issues have become more and more prominent. Logs, as a means of security recording, can still play an important role in the current security needs. However, in the face of massive log information, traditional log audit methods are stretched. Taking intrusion detection detection as an example, according to Julisch's investigation, as early as 2000, when the network was not inflated, the system generally triggered at least 3 alarm logs per minute. Now Logs have long belonged to the category of big data. Massive data will bring great difficulties to decision-making analysis, and manual analysis is not only labor-intensive but also error-prone. Nowadays, using the clustering method in data mining to mine network data has become the mainstream. ...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the existing log audit method cannot effectively audit massive logs and find abnormal data in the log, the present invention proposes a log audit method based on hierarchical clustering

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