Detection method for abnormal user in social network based on network mapping

Abstract

The invention relates to a detection method for an abnormal user in a social network based on network mapping. The method comprises the following steps of S1, crawling user data of a social network platform by using a web crawler technology; S2, preprocessing the crawled user data, and building a user social relation network graph G; S3, using node2vec to convert the social relation of a user intoa low-dimensional vector representation based on the user social relation network graph G; S4, fusing the multidimensional vector representations of the user to acquire a final vector representation;and S5, clustering based on feature vectors of the user, predicting the user is a normal user or the abnormal user, and if the user is the abnormal user, giving an abnormal type. The method providedby the invention has the advantages that the time and labor cost overheads are low, the various abnormal user types can be recognized, the new abnormal user type can be recognized, the multidimensional attribute features of the user can be comprehensively considered, and the accuracy is high.

Description

technical field [0001] The invention relates to the technical field of network data detection, in particular to a method for detecting abnormal users in a social network based on network mapping. Background technique [0002] A series of excellent features such as convenience, entertainment, and real-time nature of social networks have attracted a large number of users, and a virtual society has been built in cyberspace. At the same time, the huge user base of social networks has attracted a large number of attackers. By creating a large number of fake accounts and stealing normal accounts, attackers spread rumors in social networks, or publish advertisements, phishing, pornographic information, etc., or use these accounts to maliciously increase the reputation of other accounts, such as mass follow, malicious points Like and so on. These attackers are collectively referred to as anomalous users. Therefore, abnormal user detection in social networks is of great significan...

AI Technical Summary

Problems solved by technology

Although these detection methods have been widely used in practice, these methods can only achieve good results in specific application scenarios, the generalization ability is not strong, and they are not universal, so they cannot meet the actual needs very well.

Specifically, although behavioral feature-based and content-based detection methods have high accuracy, they need to mark the sample data in advance because of the supervised learning method, which takes a lot of time and labor costs, and can only detect existing Known abnormal types, when the abnormal user changes its appearance, it cannot be accurately detected

Although the graph-based detection method has strong robustness, the accuracy rate is low, and it can only detect abnormal users who are connected with other users. It is still in the theoretical research stage.

The method based on unsupervised learning does not need to mark the sample data in advance, which saves time and labor costs, so it can quickly form a detection system and detect unknown attack behaviors, but it is not easy to distinguish different types of abnormal users

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