Detection Method for Abnormal User of Social Network Based on Behavior Characteristics

Abstract

In this paper, an anomaly user detection method based on user's basic characteristics is proposed for abnormal users in social networks. Using the G-N community discovery algorithm to divide users into orphan users and community users, combined with rough set theory, the basic characteristics of the user weight, according to the feature weight of the feature selection, the use of feature weight and feature trust value to calculate the user's credibility, and establish an exception user detection model. The experimental results show that the proposed model is suitable for detecting large-scale data sets, and has high stability and high accuracy compared with traditional methods.