{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:32:14Z","timestamp":1775068334925,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:00:00Z","timestamp":1758931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62367005"],"award-info":[{"award-number":["62367005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Central Government Guides Local Science and Technology Development Fund Project","award":["24ZYQA051"],"award-info":[{"award-number":["24ZYQA051"]}]},{"name":"Major Cultivation Project of Scientific Research and Innovation Platforms in Gansu Provincial Colleges and Universities","award":["2024CXPT-17"],"award-info":[{"award-number":["2024CXPT-17"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Entropy"],"abstract":"<jats:p>In recent years, telecom fraud remains prevalent in many regions, severely impacting people\u2019s daily lives and causing substantial economic losses. However, previous research has mainly relied on expert knowledge for feature engineering, which lags behind and struggles to adapt to the continuously evolving patterns of fraud effectively. In addition, the extreme imbalance in fraud amounts within real communication data hinders the development of deep learning methods. In response, we propose a feature transformation method to represent users\u2019 communication behavior as comprehensively as possible, and develop a convolutional neural network (CNN) with a Focal Loss function to identify rare fraudulent activities in highly imbalanced data. Experimental results on a real-world dataset show that, under conditions of severe class imbalance, the proposed method significantly outperforms existing approaches in two key metrics: recall (0.7850) and AUC (0.8662). Our work provides a new approach for telecommunication fraud detection, enabling the effective identification of fraudulent numbers.<\/jats:p>","DOI":"10.3390\/e27101013","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:00:32Z","timestamp":1759132832000},"page":"1013","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Image-Based Telecom Fraud Detection Method Using an Attention Convolutional Neural Network"],"prefix":"10.3390","volume":"27","author":[{"given":"Jiyuan","family":"Li","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"given":"Jianwu","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"given":"Yangping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]},{"given":"Jingyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,27]]},"reference":[{"key":"ref_1","first-page":"198","article-title":"Construction and application of knowledge-base in telecom fraud domain","volume":"14","author":"Zhu","year":"2021","journal-title":"Int. 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