Abstract
Sybil attacks are increasingly rampant in online social networks (OSNs); thus, Sybil detection is one of the key issues in OSN security research. Sybils in OSNs are often used by attackers for public opinion intervention, topic flow filling, and dissemination of false and malicious messages. Therefore, if the credibility of the Sybil can be analyzed, then the harm of Sybil attacks can be prevented to a certain extent. Based on the analysis of existing Sybil detection research, this paper proposes an end-to-end Sybil detection model based on the Bidirectional Encoder Representations from Transformers (BERT) model that analyzes tweet text content. Considering the problems of the existing datasets, we built a dataset for text content analysis of tweets based on the hot political topic of the 2020 US presidential election. Accordingly, this study used a distilled version of BERT, DistilBERT, as the sentence embedding model, and the double self-normalizing long short-term memory (Double-SN-LSTM) recurrent neural network model as the classification detection model. The final experimental effect was greatly improved compared with the existing analysis methods, and it had a better detection effect for the more concealed Sybils.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant (No. 61872254), and the Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security) (No.C20606), and the Sichuan Science and Technology Program (2021JDRC0004). Xiaojie Xu and Jian Dong contribute equally to this work. We want to convey our grateful appreciation to the corresponding author of this paper, Jin Yang. He has offered advice with huge values in all stages when writing this essay to us.
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Xu, X., Dong, J., Liu, Z., Yang, J., Wang, B., Wang, Z. (2021). A Sybil Detection Method in OSN Based on DistilBERT and Double-SN-LSTM for Text Analysis. In: Garcia-Alfaro, J., Li, S., Poovendran, R., Debar, H., Yung, M. (eds) Security and Privacy in Communication Networks. SecureComm 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 399. Springer, Cham. https://doi.org/10.1007/978-3-030-90022-9_4
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DOI: https://doi.org/10.1007/978-3-030-90022-9_4
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