Use of Dempster-Shafer Theory and Bayesian Inferencing for Fraud Detection in Mobile Communication Networks

This paper introduces a framework for fraud detection in mobile communication networks based on the current as well as past behavioral pattern of subscribers. The proposed fraud detection system (FDS) consists of four components, namely, rule-based deviation detector, Dempster-Shafer component, call history database and Bayesian learning. In the rule-based component, we determine the suspicion level of each incoming call based on the extent to which it deviates from expected call patterns. Dempster-Shafer's theory is used to combine multiple evidences from the rule-based component and an overall suspicion score is computed. A call is classified as normal, abnormal, or suspicious depending on this suspicion score. Once a call from a mobile phone is found to be suspicious, belief is further strengthened or weakened based on the similarity with fraudulent or genuine call history using Bayesian learning. Our experimental results show that the method is very promising in detecting fraudulent behavior without raising too many false alarms.

[1]  Thomas M. Chen,et al.  Dempster-Shafer theory for intrusion detection in ad hoc networks , 2005, IEEE Internet Computing.

[2]  Paola Britos,et al.  Detecting Fraud in Mobile Telephony Using Neural Networks , 2005, IEA/AIE.

[3]  John Shawe-Taylor,et al.  Novel Techniques for Fraud Detection in Mobile Telecommunication Networks , 2007 .

[4]  Volker Tresp,et al.  Fraud detection in communication networks using neural and probabilistic methods , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[5]  John Shawe-Taylor,et al.  Detecting Cellular Fraud Using Adaptive Prototypes. , 1997, AAAI 1997.

[6]  John Shawe-Taylor,et al.  An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection , 2001, J. Parallel Distributed Comput..

[7]  Ramón García-Martínez,et al.  Unusual Changes of Consumption Detection in Mobile Phone Users , 2006 .

[8]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[9]  Ray J. Frank,et al.  The detection of fraud in mobile phone networks , 1996 .

[10]  Tom Fawcett,et al.  Combining Data Mining and Machine Learning for Effective User Profiling , 1996, KDD.

[11]  Yizhak Idan,et al.  Discovery of fraud rules for telecommunications—challenges and solutions , 1999, KDD '99.

[12]  D. Hand,et al.  Unsupervised Profiling Methods for Fraud Detection , 2002 .

[13]  Joos Vandewalle,et al.  Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype , 1997, ICANN.

[14]  Chang-Tien Lu,et al.  Survey of fraud detection techniques , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[15]  Peter Hoath.  Telecoms fraud, the gory details , 1998 .

[16]  John Shawe-Taylor,et al.  Novel techniques for fraud detection in mobile communications , 1996 .

[17]  Xingyu Wang,et al.  Distributed intrusion detection system based on data fusion method , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[18]  Gadi Pinkas,et al.  Unsupervised Profiling for Identifying Superimposed Fraud , 1999, PKDD.