Neural Fraud Detection in Mobile Phone Operations

Abstract

With the increasing popularity of wireless and mobile networks, and the needs in the rapidly (hanging telecommunications industry, the security issue for mobile users could be even more serious than we expect. In this paper, we present an on-line security system for fraud detection of impostors and improper use of mobile phone operations based on a neural network classifier, It acts solely on the recent information and past history of the mobile phone owner activities, and classifies the telephone users into classes according to their usage logs. Such logs contain the relevant characteristics for every call made by the user. As soon as the system identifies a fraud, it notifies both the carrier telecom and the victim about it immediately and not at the end of the monthly bill cycle. In our implementation, we make use of Kohonen model because of its simplicity and its flexibility to adapt to pattern changes. Our results indicate that our system reduces significantly the telecom carrier’s profit losses as well as the damage that might be passed to the clients. This might help the carriers to reduce significantly the cost of phone calls and will turn to the users’ advantage.

Supported by UNT Faculty Research Grant