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Fraud Detection Using an Adaptive Neuro-Fuzzy Inference System in Mobile Telecommunication Networks

机译:移动电信网络中使用自适应神经模糊推理系统的欺诈检测

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GSM (Global Services of Mobile Communications) 1800 licenses were granted in the beginning of the 2000's in Turkey. Especially in the installation phase of the wireless telecom services, fraud usage can be an important source of revenue loss. Fraud can be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is the name of the activities to identify unauthorized usage and prevent losses for the mobile network operators'. Mobile phone user's intentions may be predicted by the call detail records (CDRs) by using data mining (DM) techniques. This study compares various data mining techniques to obtain the best practical solution for the telecom fraud detection and offers the Adaptive Neuro Fuzzy Inference (ANFIS) method as a means to efficient fraud detection. In the test run, shown that ANFIS has provided sensitivity of 97% and specificity of 99% , where it classified 98.33% of the instances correctly.
机译:GSM(全球移动通信服务)1800许可证是在2000年代初在土耳其获得的。特别是在无线电信服务的安装阶段,欺诈行为可能是造成收入损失的重要来源。欺诈可以被定义为不诚实或非法使用服务,目的是避免收取服务费用。欺诈检测是识别未经授权的使用并防止移动网络运营商损失的活动的名称。可以通过使用数据挖掘(DM)技术通过呼叫详细记录(CDR)来预测移动电话用户的意图。这项研究比较了各种数据挖掘技术以获得电信欺诈检测的最佳实用解决方案,并提供了自适应神经模糊推理(ANFIS)方法作为有效欺诈检测的一种手段。在测试运行中,表明ANFIS提供了97%的灵敏度和99%的特异性,其中正确分类了98.33%的实例。

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