Journal of University of Chinese Academy of Sciences >
Detection of socialbot networks based on population characteristics
Received date: 2013-10-15
Revised date: 2013-12-16
Online published: 2014-09-15
An adversary can infiltrate online social networks (OSNs) on a large scale by deploying socialbot network, which is an army of socialbot accounts. This will endanger the information security of online social network and users. To solve the problem, we propose a detection method based on the population characteristics. We extract the following population characteristics: centralized created time, similar screen names, and coincident active time. On the basis of the extracted charateristics and by using date mining method, the method is proposed to detect socialbots networks. The method is used in a data set of 480 000 users of sina microblog and detects many socialbots networks which include 6 899 socialbots accounts. The low false negative rate and false positive rate indicate that the method is feasible and effective.
NI Ping , ZHANG Yuqing , WEN Guanxing , LIU Qixu , FAN Dan . Detection of socialbot networks based on population characteristics[J]. Journal of University of Chinese Academy of Sciences, 2014 , 31(5) : 691 -700 . DOI: 10.7523/j.issn.2095-6134.2014.05.016
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