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A Rule-based Approach for Anomaly Detection in Subscriber Usage Pattern

Authors: Rupesh K. Gopal, Saroj K. Meher


In this report we present a rule-based approach to detect anomalous telephone calls. The method described here uses subscriber usage CDR (call detail record) data sampled over two observation periods: study period and test period. The study period contains call records of customers- non-anomalous behaviour. Customers are first grouped according to their similar usage behaviour (like, average number of local calls per week, etc). For customers in each group, we develop a probabilistic model to describe their usage. Next, we use maximum likelihood estimation (MLE) to estimate the parameters of the calling behaviour. Then we determine thresholds by calculating acceptable change within a group. MLE is used on the data in the test period to estimate the parameters of the calling behaviour. These parameters are compared against thresholds. Any deviation beyond the threshold is used to raise an alarm. This method has the advantage of identifying local anomalies as compared to techniques which identify global anomalies. The method is tested for 90 days of study data and 10 days of test data of telecom customers. For medium to large deviations in the data in test window, the method is able to identify 90% of anomalous usage with less than 1% false alarm rate.

Keywords: Subscription fraud, fraud detection, anomalydetection, maximum likelihood estimation, rule based systems.

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[1] CFCA press release,, 2006 (last accessed on October 18, 2007).
[2] Shawe-Taylor, J., Howker, K., Gosset, P., Hyland, M., Verrelst, H., Moreau, Y., et al, "Novel techniques for profiling and fraud in mobile telecommunications," In P. J. G. Lisboa, B. Edisbury, A. Vellido (Eds.), Business Applications of Neural Networks. The State-of-the-art of Real World Applications, pp. 113-139, Singapore: World Scientific.
[3] Hoath. P, "What-s new in telecoms fraud?," Computer Fraud and Security, Vol. 1, pp. 10-14, 1998.
[4] Estevez, P. A, Held, C. M., Perez, C. A., "Subscription Fraud Prevention in Telecommunications using Fuzzy Rules and Neural Networks," Expert Systems with Applications, Vol. 31, pp. 337-344, 2006.
[5] Fawcett, T., Provost, F., "Combining Data Mining and Machine Learning for User Profiling," In AI approaches to fraud detection and risk management, workshop technical report WS-97-07, pp. 14-19, AAAI Press, 1997.
[6] Taniguchi, M., Haft, M., Hollmen, J., Tresp, V., "Fraud Detection in Communication Networks using Neural and Probabilistic Methods," IEEE International Conference in Acoustics, Speech and Signal Processing, Vol. 2, pp. 1241-4, 1998.
[7] Fawcett, T., Provost, F., "Adaptive Fraud Detection," Data Mining and Knowledge Discovery, Vol. 1, pp. 291-316, 1997.
[8] Hollmen, J., "Novelty Filter for Fraud Detection in Mobile Communications Networks," Technical Report submitted to Department of Computer Science and Engineering, Helsinki University of Technology, 1997.
[9] Burge, P., Shawe-Taylor, J., "Detecting cellular fraud using adaptive prototypes," AAAI Workshop on AI Approaches to Fraud Detection and Risk Management. AAAI Press, Menlo Park, CA.
[10] Burge, P., Shawe-Taylor, J., "An Unsupervised Neural Network Approach to Profiling the Behaviour of Mobile Phone Users for Use in Fraud Detection," Journal of Parallel and Distributed Computing, Vol. 61, 2001.
[11] Grabec, I, "Modelling of Chaos by a Self-Organizing Neural Network," Proceedings of International Conference on Artificial Neural Networks, Vol. 1, pp. 151-156, Elsevier publications, 1989.
[12] Moreau, Y., Vandewalle, J., "Detection of mobile phone fraud using supervised neural networks: A first prototype," International Conference on Artificial Neural Networks, 1065- 1070. Springer, Berlin., 1997.
[13] Moreau, Y., "A hybrid system for fraud detection in mobile communication," European Symposium on Artificial Neural Networks, pp. 447-454, 1999.
[14] Cortes, C., Prebigon, D., "Signature-Based Methods for Data Streams," Data Mining and Knowledge Discovery, pp. 167-182, 2001.
[15] Cortes, C., Prebigon, D., Volinsky, C., "Communities of Interest," Intelligent Data Analysis 2001, pp. 105-114, 2001.
[16] Cortes, C., Prebigon, D., Volinsky, C., "Computational Methods for Dynamic Graphs," Journal of Computational and Graphical Statistics, Vol. 12, pp. 950-970, 2003.
[17] Ferreira, et al. "Establishing Fraud Detection Patterns Based on Signatures," Industrial Conference on Data Mining, LNAI Springer- Verlag, 2006.
[18] Grosser, et al. "Detecting Fraud in Mobile Telephony Using Neural Networks," Lecture Notes in AI 3533, Springer-Verlag, 2005
[19] Cahill, M. H., Lambert, D., Pinheiro, J. C., Sun, D. X. "Detecting Fraud in Real World", In J. Abello, P. M. Pardalos, M. G. C. Resende (Eds.), Handbook of Massive Datasets, pp. 913-930, Kluwer Academic Publishers, 2002.
[20] Rosset, S., Neumann, E., Eick, U., Vatnik, N., "Customer LTV modelling and its use for Customer Retention Planning", Data Mining and Knowledge Discovery, Vol. 7, pp. 321-339,2003.
[21] Samfat, D., Molva, R., "IDAMN: an intrusion detection architecture for mobile networks", IEEE Journal on Selected areas of Communications, Vol. 15, pp. 1373-1380, 1997.
[22] Duda, R. O., Hart, P. E., Stork, D. G., "Pattern Classification", Second edition, John-Wiley and Sons, 2001.