Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud
Telecommunication fraud has led to significant economic losses worldwide. Although Graph Neural Networks (GNNs) have shown potential in fraud detection, their performance in telecom fraud detection remains suboptimal. The primary reason for this inadequacy is the inability of previous methods to det...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10643627/ |
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| author | Peiwen Gao Zhihua Li Dibin Zhou Liang Zhang |
| author_facet | Peiwen Gao Zhihua Li Dibin Zhou Liang Zhang |
| author_sort | Peiwen Gao |
| collection | DOAJ |
| description | Telecommunication fraud has led to significant economic losses worldwide. Although Graph Neural Networks (GNNs) have shown potential in fraud detection, their performance in telecom fraud detection remains suboptimal. The primary reason for this inadequacy is the inability of previous methods to detect “fraud leaders” within fraudulent groups. These leaders orchestrate scams through intermediaries rather than directly engaging in fraudulent activities. Additionally, the significantly smaller number of fraudulent users than benign users further complicates the detection process. To address these challenges, this paper proposes a Reinforced Cost-sensitive Graph Network (RCGN) for detecting fraud leaders in telecom fraud. The model first constructs a graph from users’ call detail records (CDR) and then generates predictions using three base classifiers: GCN, GAT, and GraphSAGE. During the training of the base classifiers, Adaptive Cost-Sensitive Learning (AdaCost) is employed to prioritize fraudulent nodes. Finally, weight coefficients are dynamically optimized using the Deep Deterministic Policy Gradient (DDPG) algorithm, and the prediction results of the three base classifiers are combined to produce the final classification outcomes. Experimental results on extensive real-world datasets demonstrate the effectiveness of RCGN, as it outperforms state-of-the-art GNN and GNN-based fraud detection models. |
| format | Article |
| id | doaj-art-dfe3ee3f90524628b5c7604371a29f23 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dfe3ee3f90524628b5c7604371a29f232025-08-20T02:41:20ZengIEEEIEEE Access2169-35362024-01-011217363817364610.1109/ACCESS.2024.344826010643627Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom FraudPeiwen Gao0Zhihua Li1Dibin Zhou2Liang Zhang3https://orcid.org/0009-0003-8498-7315Institute of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaInstitute of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaInstitute of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaInstitute of Information Science and Technology, Hangzhou Normal University, Hangzhou, ChinaTelecommunication fraud has led to significant economic losses worldwide. Although Graph Neural Networks (GNNs) have shown potential in fraud detection, their performance in telecom fraud detection remains suboptimal. The primary reason for this inadequacy is the inability of previous methods to detect “fraud leaders” within fraudulent groups. These leaders orchestrate scams through intermediaries rather than directly engaging in fraudulent activities. Additionally, the significantly smaller number of fraudulent users than benign users further complicates the detection process. To address these challenges, this paper proposes a Reinforced Cost-sensitive Graph Network (RCGN) for detecting fraud leaders in telecom fraud. The model first constructs a graph from users’ call detail records (CDR) and then generates predictions using three base classifiers: GCN, GAT, and GraphSAGE. During the training of the base classifiers, Adaptive Cost-Sensitive Learning (AdaCost) is employed to prioritize fraudulent nodes. Finally, weight coefficients are dynamically optimized using the Deep Deterministic Policy Gradient (DDPG) algorithm, and the prediction results of the three base classifiers are combined to produce the final classification outcomes. Experimental results on extensive real-world datasets demonstrate the effectiveness of RCGN, as it outperforms state-of-the-art GNN and GNN-based fraud detection models.https://ieeexplore.ieee.org/document/10643627/Fraud detectionreinforcement learningdeep deterministic policy gradientcost-sensitive learning |
| spellingShingle | Peiwen Gao Zhihua Li Dibin Zhou Liang Zhang Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud IEEE Access Fraud detection reinforcement learning deep deterministic policy gradient cost-sensitive learning |
| title | Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud |
| title_full | Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud |
| title_fullStr | Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud |
| title_full_unstemmed | Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud |
| title_short | Reinforced Cost-Sensitive Graph Network for Detecting Fraud Leaders in Telecom Fraud |
| title_sort | reinforced cost sensitive graph network for detecting fraud leaders in telecom fraud |
| topic | Fraud detection reinforcement learning deep deterministic policy gradient cost-sensitive learning |
| url | https://ieeexplore.ieee.org/document/10643627/ |
| work_keys_str_mv | AT peiwengao reinforcedcostsensitivegraphnetworkfordetectingfraudleadersintelecomfraud AT zhihuali reinforcedcostsensitivegraphnetworkfordetectingfraudleadersintelecomfraud AT dibinzhou reinforcedcostsensitivegraphnetworkfordetectingfraudleadersintelecomfraud AT liangzhang reinforcedcostsensitivegraphnetworkfordetectingfraudleadersintelecomfraud |