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Deussom Djomadji, E.M., Matemtsap Mbou, B., Tchagna Kouanou, A., Ekonde Sone, M. and Bayonbog, P. (2022) Machine Learning-Based Approach for Designing and Implementing a collaborative Fraud Detection Model through CDR and Traffic Analysis. Transactions on Engineering and Computing Sciences, 10, 46-58.
https://doi.org/10.14738/tmlai.104.12854

has been cited by the following article:

• TITLE: Machine Learning-Based Approach for Identification of SIM Box Bypass Fraud in a Telecom Network Based on CDR Analysis: Case of a Fixed and Mobile Operator in Cameroon

  AUTHORS: Eric Michel Deussom Djomadji, Kabiena Ivan Basile, Tchapga Tchito Christian, Ferry Vaneck Kouam Djoko, Michael Ekonde Sone

  KEYWORDS: CDR, Fraud Detection, Machine Learning, Voice Calls

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.2, February 28, 2023

  ABSTRACT: In the telecommunications sector, companies suffer serious damages due to fraud, especially in Africa. One of the main types of fraud is SIM box bypass fraud, which includes using SIM cards to divert incoming international calls from mobile operators creating massive losses of revenue. In order to provide a solution to these shortcomings that apply almost to all network operators, we developed intelligent algorithms that exploit huge amounts of data from mobile operators and that detect fraud by analyzing CDRs from voice calls. In this paper we used three classification techniques: Random Forest, Support Vector Machine (SVM) and XGBoost to detect this type of fraud; we compared the performance of these different algorithms to evaluate the model by using data collected from an operator’s network in Cameroon. The algorithm that produced a better performance was the Random Forest with 92% accuracy, so we effectuated the detection of existing fraudulent numbers on the telecommunications operator’s network.