Mobile network Fraud Detection Using Artificial Neural Networks

Abstract

The past decade has witnessed the rapid deployment and evolution of mobile cellular networks, which now support billions of users and a vast diverse array of mobile devices from smartphones, tablets, to e-readers and smart meters Due to this high number of mobile devices and low mobile phone service connection rates Mobile phone communication is now faced with two major threats which are Voice-related security threats, ranging from conventional voice scams similar to those on landlines, e.g., stealing customers privacy information or defrauding users of money through various social engineering techniques, the new forms of voice fraud that utilize the data functionality of smartphones for voice-related trickeries. The other threat is the SMS-related security threats which range from sending threatening messages to other mobile phone users to extort money from them to sending ‘false win’ messages to other subscribers and demand funds in return. Detecting and rooting out voice-related and SMS-related fraud activities, is not an easy task, due to the large user population, the vast phone number space and limited data which is recorded when a call is made or an SMS is send. The objection of this study was to determine the performance of Artificial Neural Networks in classifying and detecting the fraud rent activities. We developed a system that uses Artificial Neural Network to classify phone numbers and detect the once being involved in the fraud rent activities using the call attributes captured by the mobile service provider. The system was tested using the data captured in a span of three months and the results compared with actual fraud rent cases reported to the service provider. In the model different time variant datasets were used to train the network and perform the classification. Also training the Network using different size variant dataset was performed. This was to examine the correct data size and age that is optimum and accurate in the classification. We found out that Artificial Neural Network was an optimum tool when it comes to classifying these fraud rent activities due to its ability to dynamically learn fraud rent patterns that change day by day. We also found out that Training data size and age were major factors that affected the accuracy in classification.