Please use this identifier to cite or link to this item: http://dspace.univ-guelma.dz/jspui/handle/123456789/16454
Title: SIMBox Fraud Detection in Telecommunication Operators: A case Study on "Djezzy "
Authors: BOUGUETTOUCHA, AHMED RAMI
Keywords: SIM Box Fraud, SIM Box Fraud Detection, Data Balancing, Call Detail Record, Machine Learning
Issue Date: 2024
Publisher: University of guelma
Abstract: The SIMBox is a telecommunications device that reroutes international voice calls through the internet to a local SIM card within the device, making them appear as local calls and bypassing standard network gateways. This Master’s thesis presents a SIMBox Fraud Detector, an AI-powered web application for SIMBox fraud detec- tion in mobile telecommunications networks. A thorough experiments with machine learning techniques using the dataset CDR (Call Detail Records) from Djezzy mo- bile operator show that the XGBoost model with Tomek+RUS undersampling exhibits promising advantages in both detection and balance performance. XGBoost not only achieved a precision and accuracy of 96% for fraudulent detection, but it also pro- vided the best tradeoff between false positive rate and true positive rate.
URI: http://dspace.univ-guelma.dz/jspui/handle/123456789/16454
Appears in Collections:Master

Files in This Item:
File Description SizeFormat 
F5_8_BOUGUETTOUCHA_AHMED RAMI.pdf4,54 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.