TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Detecting Malicious Social Robots with Generative Adversarial Networks |
| 영문제목(English Title) |
Detecting Malicious Social Robots with Generative Adversarial Networks |
| 저자(Author) |
Bin Wu
Le Liu
Zhengge Dai
Xiujuan Wang
Kangfeng Zheng
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| 원문수록처(Citation) |
VOL 13 NO. 11 PP. 5594 ~ 5615 (2019. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English 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%.
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| 키워드(Keyword) |
malicious robots
social robots detection
generative adversarial networks
supervised classification
unbalanced data
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| 파일첨부 |
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