Journal Article10.1109/JIOT.2022.3174143
Wangiri Fraud: Pattern Analysis and Machine-Learning-Based Detection
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TL;DR: In this article , Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled, and the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set.
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Abstract: The rapid growth of the telecommunication landscape leads to a rapid rise of frauds in such networks. In this article, Wangiri fraud in which users are deceived by being charged for services without their knowledge during a call is tackled. In fact, Wangiri fraud has significant negative financial and reputation consequences for the mobile service providers and also has a bad psychological impact on the victims. In order to identify this fraudulent behavior, three Wangiri fraud patterns are defined by analyzing call records of over a year. Then, the security and performance of unsupervised and supervised machine learning (ML) methods in detecting one Wangiri pattern are evaluated using a large real-world Call Detail Records (CDRs) data set. In the context of Wangiri fraud detection, classification algorithms outperformed the others based on the chosen security and performance metrics. Finally, the performance evaluation of these algorithms is extended in detecting the other two real-world Wangiri fraud patterns. This article provides a detailed definition of the Wangiri fraud patterns and outlines the implementation and evaluation of ML algorithms in the context of detecting Wangiri fraud. The security analysis and experimental results demonstrate that depending on fraud patterns the best ML algorithm to detect Wangiri fraud may also vary.
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Citations
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
TL;DR: Wang et al. as discussed by the authors presented a novel Cost-Sensitive Graph Neural Network (CSGNN) by creatively combining cost-sensitive learning and graph neural networks to solve the graph imbalance problem and then achieve better detection performance than the state-of-the-art algorithms.
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
TL;DR: Wang et al. as discussed by the authors proposed a graph attention network with COst-sensitive learning (GAT-COBO) for the graph imbalance problem, which used a GAT-based base classifier to learn the embeddings of all nodes in the graph.
Telecom Fraud Detection via Imbalanced Graph Learning
Xinxin Hu,Haotian Chen,Hongchang Chen,Shuxin Liu,Xing Li +4 more
- 11 Nov 2022
TL;DR: Wang et al. as mentioned in this paper designed a new GNN-based fraud detector, which transforms the node features with a multilayer perceptron and uses reinforcement learning-based neighbor sampling strategy to balance different classes of node neighbors.
3
Machine Learning Models for Fraud Detection: A Comprehensive Review and Empirical Analysis
Vishakha D. Akhare, L. K. Vishwamitra
TL;DR: This comprehensive research gives academics and companies a foundation for better, more effective and more scalable fraud detection systems in this period of essential digital security.
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