Website classification method based on network relation graph

Abstract

The invention discloses a website classification method based on a network relation graph. The website classification method comprises the following steps: using the malicious data of 'security alliance' as a sample data source and obtaining existing data classification and URL data; forming a site mapping table and a word frequency analysis table, extracting a fingerprint feature of a sample for constructing a feature table and forming a type table; forming an undirected weight graph by each site and a connection weight thereof; constituting a network relation graph by numerous sites and weights, and dividing the big network relation graph into several sub-graphs through a map clustering algorithm; extracting and classifying the fingerprint features of each task unit on a server thereof through a site fingerprint feature extractor and a site fingerprint feature classifier. According to the website classification method based on the network relation graph, the detection speed of website classification is remarkably improved, and the specific type is distinguished. For the newly added site, a processing unit is determined according to the connection relation of the newly added site and the existing site, the computational complexity and the number of iterations of the training are thus effectively reduced, and the computing resources are saved.

Description

technical field [0001] The invention relates to the field of Internet website classification, in particular to a website classification method based on a network relation graph. Background technique [0002] The Internet is an open sharing platform. With the continuous development of the Internet, the scale of web pages continues to expand, and various web pages continue to emerge. Traditional network security agencies usually use manual review system judgment and heuristic crawler automatic identification to classify the emerging websites. But either way, there are serious challenges. First of all, the ever-expanding scale of webpage data makes it difficult for existing technologies to cover all webpages, and the speed of data processing is far behind the update speed of webpages. The disadvantage of manual review is that the effect of classification depends on the professional ability of reviewers, and the processing cycle is relatively long. Secondly, malicious URLs of...

AI Technical Summary

Problems solved by technology

For LAN users, their behavior habits often have a lot in common, which also brings limitations in access vision

Secondly, because this method relies on user behavior for analysis, it can only distinguish whether the website is malicious, and it is difficult to distinguish the malicious type of the website.

More difficult to classify general sites

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