Please use this identifier to cite or link to this item:
https://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 | |
|---|---|---|---|---|
| F5_8_BOUGUETTOUCHA_AHMED RAMI.pdf | 4,54 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.