Parallel Outlier Detection in Dial-Back Fraud Calls

Abstract

With the intensified competition among telecommunications industry, we focused much on the quality of service. Illegal activities, especially dial-back fraud calls, may cause annoyance and inconvenience which will reduce user experience. The detection of dial-back fraud calls is an urgent issue that needs to be addressed. The rapid development of information technology which gives rise to the accumulated huge data will pose a greater challenge. However, traditional detecting methods to identify illegal activities cannot get acceptable accuracy. On the other hand, those methods become very inefficient or even unavailable when processing massive data. In this paper, we introduce a distributed outlier detection approach to locate illegal acts of the illegal users who have the characteristics as outliers. For a higher hit rate, we combine outlier detection with cluster coefficient. Besides, the method exploits parallel computation based on MapReduce in order to obtain vast time savings and improve the processing capability of the algorithm on large data. Extensive experimental results demonstrate the efficiently performances of proposed algorithm according to the evaluation criterions of speedup and scale up.