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- W4387145712 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." @default.
- W4387145712 created "2023-09-29" @default.
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- W4387145712 date "2023-01-01" @default.
- W4387145712 modified "2023-10-18" @default.
- W4387145712 title "A Novel Method for Spam Call Detection Using Graph Convolutional Networks" @default.
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- W4387145712 doi "https://doi.org/10.1007/978-3-031-42430-4_9" @default.
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