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http://dx.doi.org/10.3837/tiis.2019.11.018

Detecting Malicious Social Robots with Generative Adversarial Networks  

Wu, Bin (School of Cyberspace Security, Beijing University of Posts and Telecommunications)
Liu, Le (School of Cyberspace Security, Beijing University of Posts and Telecommunications)
Dai, Zhengge (Telecommunication Engineering with Management, Beijing University of Posts and Telecommunications)
Wang, Xiujuan (Faculty of Information Technology, Beijing University of Technology)
Zheng, Kangfeng (School of Cyberspace Security, Beijing University of Posts and Telecommunications)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.11, 2019 , pp. 5594-5615 More about this Journal
Abstract
Malicious social robots, which are disseminators of malicious information on social networks, seriously affect information security and network environments. The detection of malicious social robots is a hot topic and a significant concern for researchers. A method based on classification has been widely used for social robot detection. However, this method of classification is limited by an unbalanced data set in which legitimate, negative samples outnumber malicious robots (positive samples), which leads to unsatisfactory detection results. This paper proposes the use of generative adversarial networks (GANs) to extend the unbalanced data sets before training classifiers to improve the detection of social robots. Five popular oversampling algorithms were compared in the experiments, and the effects of imbalance degree and the expansion ratio of the original data on oversampling were studied. The experimental results showed that the proposed method achieved better detection performance compared with other algorithms in terms of the F1 measure. The GAN method also performed well when the imbalance degree was smaller than 15%.
Keywords
malicious robots; social robots detection; generative adversarial networks; supervised classification; unbalanced data;
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