{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:28:02Z","timestamp":1772792882117,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Scientific and Technological Special Project of Henan Province","award":["221100210700"],"award-info":[{"award-number":["221100210700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Telecom fraud detection is of great significance in online social networks. Yet the massive, redundant, incomplete, and uncertain network information makes it a challenging task to handle. Hence, this paper mainly uses the correlation of attributes by entropy function to optimize the data quality and then solves the problem of telecommunication fraud detection with incomplete information. First, to filter out redundancy and noise, we propose an attribute reduction algorithm based on max-correlation and max-independence rate (MCIR) to improve data quality. Then, we design a rough-gain anomaly detection algorithm (MCIR-RGAD) using the idea of maximal consistent blocks to deal with missing incomplete data. Finally, the experimental results on authentic telecommunication fraud data and UCI data show that the MCIR-RGAD algorithm provides an effective solution for reducing the computation time, improving the data quality, and processing incomplete data.<\/jats:p>","DOI":"10.3390\/e25010112","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T04:12:56Z","timestamp":1672891976000},"page":"112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3737-2660","authenticated-orcid":false,"given":"Ran","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongchang","family":"Chen","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxin","family":"Liu","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0566-9198","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8007-5122","authenticated-orcid":false,"given":"Xinxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","first-page":"973","article-title":"CCF Based System Framework In Federated Learning Against Data Poisoning Attacks","volume":"26","author":"Ahmed","year":"2022","journal-title":"J. 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