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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 | Size | Format | |
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F5_8_BOUGUETTOUCHA_AHMED RAMI.pdf | 4,54 MB | Adobe PDF | View/Open |
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