Fraud in telephony incurs huge revenue losses and causes a menace to both the service providers and legitimate users. This problem is growing alongside augmenting technologies. Yet, the works in this area are hindered by the availability of data and confidentiality of approaches. In this work, we deal with the problem of detecting different types of unsolicited users from spammers to fraudsters in a massive phone call network. Most of the malicious users in telecommunications have some of the characteristics in common. These characteristics can be defined by a set of features whose values are uncommon for normal users. We made use of graph-based metrics to detect profiles that are significantly far from the common user profiles in a real data log with millions of users. To achieve this, we looked for the high leverage points in the 99.99th percentile, which identified a substantial number of users as extreme anomalous points. Furthermore, clustering these points helped distinguish malicious users efficiently and minimized the problem space significantly. Convincingly, the learned profiles of these detected users coincided with fraudulent behaviors.

电话欺诈会造成巨大的收入损失，并给服务提供商和合法用户带来威胁。随着增强技术的发展，这个问题越来越严重。但是，该领域的工作受到数据可用性和方法保密性的阻碍。在这项工作中，我们处理的问题是检测大规模电话网络中从垃圾邮件发送者到欺诈者的不同类型的未经请求的用户。电信中的大多数恶意用户具有一些共同的特征。这些特征可以由一组功能定义，这些功能的值对于普通用户而言并不常见。我们利用基于图形的指标来检测与数百万用户的真实数据日志中的普通用户配置文件相距甚远的配置文件。为此，我们在99中寻找高杠杆点。第99个百分位，将大量用户标识为极端异常点。此外，将这些点聚类有助于有效区分恶意用户，并最大程度地减少了问题空间。令人信服的是，这些检测到的用户的学习资料与欺诈行为相吻合。