期刊
IEEE ACCESS
卷 7, 期 -, 页码 28855-28862出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2901864
关键词
Online social network; social bots; user behavior; semi-supervised clustering
资金
- National Natural Science Foundation of China [61772174, 61370220]
- Plan For Scientific Innovation Talent of Henan Province [174200510011]
- Program for Innovative Research Team (in Science and Technology) in the University of Henan Province [15IRTSTHN010]
- Program for Henan Province Science and Technology [142102210425]
- Natural Science Foundation of Henan Province [162300410094]
- Project of the Cultivation Fund of Science and Technology Achievements of Henan University of Science and Technology [2015BZCG01]
With the significant increase in the volume, velocity, and variety of user data (e.g., user-generated data) in online social networks, there have been attempted to design new ways of collecting and analyzing such big data. For example, social bots have been used to perform automated analytical services and provide users with improved quality of service. However, malicious social bots have also been used to disseminate false information (e.g., fake news), and this can result in real-world consequences. Therefore, detecting and removing malicious social bots in online social networks is crucial. The most existing detection methods of malicious social bots analyze the quantitative features of their behavior. These features are easily imitated by social bots; thereby resulting in low accuracy of the analysis. A novel method of detecting malicious social bots, including both features selection based on the transition probability of clickstream sequences and semi-supervised clustering, is presented in this paper. This method not only analyzes transition probability of user behavior clickstreams but also considers the time feature of behavior. Findings from our experiments on real online social network platforms demonstrate that the detection accuracy for different types of malicious social bots by the detection method of malicious social bots based on transition probability of user behavior clickstreams increases by an average of 12.8%, in comparison to the detection method based on quantitative analysis of user behavior.
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