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Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks

机译:模糊聚类和支持向量机在移动电信网络欺诈检测中的应用

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摘要

This paper addresses the problem of finding out fraudulent calls in mobile phones by analysing the user's calling behaviour. In this work, we have used support vector machine (SVM) along with fuzzy clustering for detecting fraudulent usage of mobile phones. The reality mining data-set has been used for testing the efficacy of the proposed approach. A total of five relevant features are being used in creating the user profile from the user's call record. Fuzzy clustering is applied for generating the SVM classifier model. An anomaly is detected when a call pattern does not match with any of the normal patterns. Our experiments show promising results in terms of finding fraudulent calls without raising too many false alarms. Comparative studies are carried out on the proposed system by applying different types of SVMs along with various fuzzy clustering techniques for analysing the performance of the system.
机译:本文通过分析用户的通话行为来解决在手机中查找欺诈性电话的问题。在这项工作中,我们将支持向量机(SVM)与模糊聚类一起用于检测手机的欺诈性使用。现实挖掘数据集已用于测试所提出方法的有效性。根据用户的通话记录创建用户个人资料时,总共使用了五个相关功能。模糊聚类用于生成SVM分类器模型。当呼叫模式与任何正常模式都不匹配时,将检测到异常。我们的实验在发现欺诈性呼叫而不会引发太多错误警报方面显示出令人鼓舞的结果。通过应用不同类型的SVM以及各种模糊聚类技术对系统性能进行分析,对提议的系统进行了比较研究。

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