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Exploiting Spatial-Temporal Behavior Patterns for Fraud Detection in Telecom Networks
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AI
DOI：10.1109/TDSC.2022.3228797.png)
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Abstract
En 中文
Fraud detection in telecom network is a crucial problem that threatens users' privacy and property security. In recent years, fraudsters adopt more advanced camouflage strategies to avoid being detected by traditional algorithms. To deal with these new types of fraud, it is necessary to analyze the integrated spatial-temporal features, which are rarely involved in existing literature. In this article, we propose a novel fraud detection model based on the intertwined spatial-temporal patterns of user behaviors. Specifically, we first introduce the extension of statistical and interactive features to dynamic call patterns, and build a probabilistic model to simulate users' call behaviors. Then the sequential patterns reflecting users' own behaviors are obtained by the mixture Hidden Markov Models, and the structural patterns reflecting the collaboration between users in the telecom network are obtained by the attention-based Graph-SAGE model. Finally, our model outputs a fraud score for each user to detect potential fraudsters. We conduct extensive experiments on a real-world telecom dataset. The experimental results demonstrate that our intertwined spatial-temporal call patterns can effectively represent user behavior and improve the accuracy of fraud detection compared with state-of-the-art methods. The results also validate the efficiency and the interpretability of our model.
Keywords:
Telecommunications
Hidden Markov models
Fraud
Behavioral sciences
Feature extraction
Privacy
Analytical models
Behavior analysis
dynamic call pattern
fraud detection
spatial-temporal model
Journal
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7.5
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2.4K
Citations:
9.6K
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