A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities-Reference-Cited by-同舟云学术

A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities

Published:2021 Issue: Volume: Page:175-183
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Mawgoud Ahmed A.^ORCID,Abu-Talleb Amr^ORCID,Tawfik Benbella S.^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-69717-4_19

Reference27 articles.

1. Jain, V.: Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining. Int. J. Inf. Technol. 9(3), 303–310 (2017) https://doi.org/10.1007/s41870-017-0036-5

2. Fraud detection gap between auditor and fraud detection models: evidence from gulf cooperation council. Asian J. Account. Governance 13, 1–13 (2020). https://doi.org/10.17576/ajag-2020-13-01

3. Guo, K., Wang, W.: Research on telecom fraud detection model based on cellular network data. J. Networking Telecommun. 2(1), p. 12 (2020). https://doi.org/10.18282/jnt.v2i1.835

4. Gangadharan, N.: What is Wangiri Fraud? How does it impact telecom operators? In: Subex Limited. https://www.subex.com/blog/What-is-wangiri-fraud-how-does-it-impact-for-telecom-operators/. Accessed 9 Mar 2020

5. Detect & Protect Against Wangiri Callback Fraud. In: Enghouse Networks. https://www.enghousenetworks.com/enghouse-resources/blog/detect-protect-against-wangiri-callback-fraud/. Accessed 15 Apr 2020

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1. Dynamic Behavior Pattern: Mining the Fraudsters in Telecom Network;2022 IEEE 24th Int Conf on High Performance Computing & Communications; 8th Int Conf on Data Science & Systems; 20th Int Conf on Smart City; 8th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys);2022-12