Sim-Boxing Fraud Detection System Using Artificial Neural Network

Abstract

ABSTRACT

Telecommunication fraud is when con artists use pretense to obtain discounted or complimentary services. Globally, prevalent telecommunication fraud results in significant yearly revenue losses for telecom firms. The SIM-box bypass fraud is one tactic scammers use to deceive telecom operators. Installing numerous prepaid SIM cards in a SIM box is known as SIM-box fraud. This makes it possible for fraudsters to place international calls using local phone numbers in the destination countries and receive calls over VoIP (the Internet). By starting the call using the local SIM that is inserted in the SIM box, it appears to be local. This study created a model that employed Artificial Neural Network (ANN) model to more accurately and efficiently identify fraudulent customers; diverted SIM-Box calls using Customer Detail Record (CDR) data. A framework using multi-layer perceptrons, a class of ANN, to identify patterns indicative of SIM-box operations by analysing Call Detail records (CDRs) was developed. The model was trained using 500 iterations on a dataset comprising labelled records of both legitimate and fraudulent calls, using features such as call duration, frequency of calls, timestamp patterns, and originating and terminating number characteristics. The range of prediction accuracy obtained from the developed artificial neural network models was 56.8% to 62.3%. The top model, consisting of five layers, yielded 62.3%. These layers consist of an input layer with neurons, five, nine, and eighteen hidden levels, and one output layer with a single neuron that stands for fraud. It is recommended that the dataset be expanded to include a wider range of SIM-box fraud scenarios from various geographic locations and over different periods. This would improve the model’s robustness and adaptability to changing fraud techniques.