Identification and early warning method for abnormal behavior website

Abstract

The invention discloses a recognition and early warning method for an abnormal behavior website, and the method comprises the steps of suspicious website clue recognition, website feature data analysis, operation subject information analysis, network public opinion data analysis, abnormal behavior website risk study and judgment and the like, and can fully guarantee a scanning range and recognition accuracy, and guarantee effective recognition of webpage text content; the method has the advantages that abnormal behavior website operation feature recognition is more accurate, the recognition rate is accurate, the network abnormal behavior suspicion of the target website is proved through multiple information levels, the network abnormal behavior risk research and judgment dimension is expanded, the abnormal behavior website recognition accuracy is effectively improved, and the false alarm rate is reduced.

Description

technical field [0001] The invention belongs to the technical field of identifying abnormal behavior websites, and in particular relates to an identification and early warning method for abnormal behavior websites. Background technique [0002] The traditional early warning identification method for abnormal behavior websites is to match keywords, and count the keyword types and frequency of keywords appearing on a certain web page to identify and early warning. However, the traditional early warning method has the following shortcomings: [0003] 1. With the increase of abnormal behavior website features and the continuous expansion of the keyword library, it is easy to generate false alarms; [0004] 2. With the update of abnormal network behavior patterns and methods, only relying on the keyword database for matching cannot keep up with the development of abnormal network behaviors, which is prone to false positives; [0005] 3. The early warning data generated by using ...

AI Technical Summary

Problems solved by technology

[0003] 1. With the increase of abnormal behavior website features and the continuous expansion of the keyword library, it is easy to generate false alarms;

[0004] 2. With the update of abnormal network behavior patterns and methods, only relying on the keyword database for matching cannot keep up with the development of abnormal network behaviors, which is prone to false positives;

[0005] 3. The early warning data generated by using only a single dimension of keywords to identify abnormal behavior websites is not convincing, and requires manual verification and analysis again, and the degree of automation of early warning identification is low

[0006] Due to the shortcomings of the traditional abnormal behavior website identification and early warning method, it cannot meet the current needs of relevant agencies for the use of abnormal behavior website early warning identification

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