Abstract
Cybercrimes and fraud techniques are major threats to telecommunications sectors in the last decade, one of those fraud approaches called Wangiri fraud. Wangiri is a common type of fraud techniques in telecommunications sector, the definition is originated from a Japanese word that means (one & cut); as the fraudsters depend on a single ring method to gain illegal money from the subscribers. Consequently, the approaches that are used to detect fraud cases are used to classify subscribers based on their behaviors such as data extraction that identifies patterns in large datasets through a combination of statistical methods, artificial intelligence and databases. Neural networks are used to process & evaluate the given datasets; in order to uncover ambiguous communication and secret data patterns. This paper proposes the usage of AI neural networks to overcome the highly predictive wangiri fraud in a telecom dataset and to make an effective and convenient classification. ISTAT was used to test both accuracy and efficiency of the proposed method.
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Mawgoud, A.A., Abu-Talleb, A., Tawfik, B.S. (2021). A Holistic Neural Networks Classification for Wangiri Fraud Detection in Telecommunications Regulatory Authorities. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_19
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