TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

Li, Ran; Chen, Hongchang; Liu, Shuxin; Wang, Kai; Wang, Biao; Hu, Xinxin

doi:10.3390/e25010112

Open AccessArticle

TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

by

Ran Li

¹

,

Hongchang Chen

^2,*,

Shuxin Liu

^2,*,

Kai Wang

²,

Biao Wang

¹

and

Xinxin Hu

¹

Institute of Information Technology, PLA Strategic Support Force Information Engineering University, Zhengzhou 450002, China

²

National Digital Switching System Engineering and Technological R&D Center, Zhengzhou 450002, China

^*

Authors to whom correspondence should be addressed.

Entropy 2023, 25(1), 112; https://doi.org/10.3390/e25010112

Submission received: 1 November 2022 / Revised: 24 December 2022 / Accepted: 3 January 2023 / Published: 5 January 2023

(This article belongs to the Special Issue Data Science: Measuring Uncertainties II)

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Abstract

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.

Keywords: telecom fraud detection; attribute reduction; incomplete information system; maximal consistent block; MCIR-RGAD

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MDPI and ACS Style

Li, R.; Chen, H.; Liu, S.; Wang, K.; Wang, B.; Hu, X. TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block. Entropy 2023, 25, 112. https://doi.org/10.3390/e25010112

AMA Style

Li R, Chen H, Liu S, Wang K, Wang B, Hu X. TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block. Entropy. 2023; 25(1):112. https://doi.org/10.3390/e25010112

Chicago/Turabian Style

Li, Ran, Hongchang Chen, Shuxin Liu, Kai Wang, Biao Wang, and Xinxin Hu. 2023. "TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block" Entropy 25, no. 1: 112. https://doi.org/10.3390/e25010112

APA Style

Li, R., Chen, H., Liu, S., Wang, K., Wang, B., & Hu, X. (2023). TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block. Entropy, 25(1), 112. https://doi.org/10.3390/e25010112

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TFD-IIS-CRMCB: Telecom Fraud Detection for Incomplete Information Systems Based on Correlated Relation and Maximal Consistent Block

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