Abstract
Unwanted calls from advertisers or phishers are a major nuisance for millions of mobile phone users. Manual blacklists are ineffective against this growing problem. A sophisticated approach is needed on the telephone operator side to detect spam calls based on malicious patterns. In this paper, we present a framework that uses network-relationship between mobile users for spam detection. Our framework has two main contributions: i) We propose an efficient and cost-effective method to construct a large, labeled spam detection graph by leveraging end-user collaboration; ii) We propose a graph neural network (GNN)-based model for the spam detection task. Our experiments show that our model outperforms strong baseline models in this research field.
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Nguyen, TK., Tran, VT., Nguyen, HA., Bui, KH.N. (2023). A Novel Method for Spam Call Detection Using Graph Convolutional Networks. In: Nguyen, N.T., et al. Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2023. Communications in Computer and Information Science, vol 1863. Springer, Cham. https://doi.org/10.1007/978-3-031-42430-4_9
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DOI: https://doi.org/10.1007/978-3-031-42430-4_9
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