Research progress of abnormal user detection technology in social network - QU Qiang, YU Hongtao, HUANG Ruiyang (网络与信息安全学报, 2018)
In social networks, the problem of anomalous users detection is one of the key problems in network security research. The anomalous users conduct false comments, cyberbullying or cyberattacks by creating multiple vests, which seriously threaten the information security of normal users and the credit system of social networks , so a large number of researchers conducted in-depth study of the issue. The research results of the issue in recent years were reviewed and an overall structure was summarized. The data collection layer introduces the data acquisition methods and related data sets, and the feature presentation layer expounds attribute features, content features, network features, activity features and auxiliary features. The algorithm selection layer introduces supervised algorithms, unsupervised algorithms and graph algorithms. The result evaluation layer elaborates the method of data annotation method and index. Finally, the future research direction in this field was looked forward.