A Novel Method for Spam Call Detection Using Graph Convolutional Networks
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-42430-4_9
Reference10 articles.
1. Chaisamran, N., Okuda, T., Blanc, G., Yamaguchi, S.: Trust-based voip spam detection based on call duration and human relationships. In: 2011 IEEE/IPSJ International Symposium on Applications and the Internet, pp. 451–456 (2011). https://doi.org/10.1109/SAINT.2011.84
2. Elizalde, B., Emmanouilidou, D.: Detection of robocall and spam calls using acoustic features of incoming voicemails. In: Proceedings of Meetings on Acoustics, p. 060004. ASA, POMA (2021), https://www.microsoft.com/en-us/research/publication/detection-of-robocall-and-spam-calls-using-acoustic-features-of-incoming-voicemails/
3. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. CoRR abs/1609.02907 (2016). arxiv.org/abs/1609.02907
4. Kontsewaya, Y., Antonov, E., Artamonov, A.: Evaluating the effectiveness of machine learning methods for spam detection. Procedia Comput. Sci. 190(C), 479–486 (2021). https://doi.org/10.1016/j.procs.2021.06.056
5. Lentzen, D., Grutzek, G., Knospe, H., Porschmann, C.: Content-based detection and prevention of spam over IP telephony - system design, prototype and first results. In: 2011 IEEE International Conference on Communications (ICC), pp. 1–5 (2011). https://doi.org/10.1109/icc.2011.5963108
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