Detection of SIM-BOX Fraud Using Deep Learning
No Thumbnail Available
Date
2025-06
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
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.
Description
Keywords
Deep learning, Bypass Fraud, Fraud Detection, SIM-Boxing