Detection of SIM-BOX Fraud Using Deep Learning
| dc.contributor.advisor | Tsegamlak Terefe (PhD)) | |
| dc.contributor.author | Haile Welay | |
| dc.date.accessioned | 2025-07-29T14:33:50Z | |
| dc.date.available | 2025-07-29T14:33:50Z | |
| dc.date.issued | 2025-06 | |
| dc.description.abstract | In underdeveloped countries, the telecommunications infrastructure is often subsidized by the high cost of incoming international calls. However, this situation has led to an increase in sim box fraud, where attackers use VoIP-GSM gateways, known as ”SIM-BOXES,” to illegally route international calls through local wired data connections. The research presented here developed models for the classification of Call Detail Records (CDRs) in order to come up with a model that identifies fraudulent subscribers with higher accuracy. Three classification techniques, viz. Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and autoencoder, combined with three user aggregation datasets (4-hour, daily, and monthly aggregated), were used. These three algorithms, along with the three datasets, were applied in building the models. Results of the work show that LSTM performed better among the three algorithms with an accuracy of 99.81% and a lesser false positive on the monthly aggregated dataset. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/5708 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | Deep learning | |
| dc.subject | Bypass Fraud | |
| dc.subject | Fraud Detection | |
| dc.subject | SIM-Boxing | |
| dc.title | Detection of SIM-BOX Fraud Using Deep Learning | |
| dc.type | Thesis |