FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection
FrauDetector: A Graph-Mining-based Framework for Fraudulent Phone Call Detection
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DOI：
10.1145/2783258.2788623
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发表时间：
2015-08
期刊：
影响因子：
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通讯作者：
V. Tseng;J. Ying;Che-Wei Huang;Yimin Kao;Kuan-Ta Chen
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文献类型：
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作者：
V. Tseng;J. Ying;Che-Wei Huang;Yimin Kao;Kuan-Ta Chen
近年来，随着现代科技和全球通讯的发展，欺诈行为迅速增加。虽然许多文献已经解决了欺诈检测问题，这些现有的工作只集中在制定欺诈检测问题作为一个二元分类问题。由于电信记录所提供的信息的限制，用于欺诈性电话呼叫检测的这种基于分类器的方法通常不能很好地工作。在本文中，我们开发了一个基于图挖掘的欺诈电话检测框架的移动的应用程序自动注释欺诈电话号码与“欺诈”标签，这是一个重要的先决条件，区分欺诈电话从正常的电话。我们的检测方法执行加权HITS算法来学习远程电话号码的信任值。基于用户的通信记录，我们构造了两种有向二部图：CPG和UPG来表示用户的通信行为。为了对CPG和UPG的边缘进行加权，我们从两个不同但互补的方面为每对用户和远程电话号码提取特征：1）用户和电话号码之间的持续时间相关性（DR）;以及2）用户和电话号码之间的频率相关性（FR）。在加权CPG和UPG之后，我们为每个远程电话号码确定信任值。最后，我们进行了全面的实验研究的基础上收集的数据集，通过反欺诈的移动的应用程序，Whoscall。结果表明，我们的加权HITS为基础的方法的有效性，并显示在特征提取中考虑DR和FR的强度。
In recent years, fraud is increasing rapidly with the development of modern technology and global communication. Although many literatures have addressed the fraud detection problem, these existing works focus only on formulating the fraud detection problem as a binary classification problem. Due to limitation of information provided by telecommunication records, such classifier-based approaches for fraudulent phone call detection normally do not work well. In this paper, we develop a graph-mining-based fraudulent phone call detection framework for a mobile application to automatically annotate fraudulent phone numbers with a "fraud" tag, which is a crucial prerequisite for distinguishing fraudulent phone calls from normal phone calls. Our detection approach performs a weighted HITS algorithm to learn the trust value of a remote phone number. Based on telecommunication records, we build two kinds of directed bipartite graph: i) CPG and ii) UPG to represent telecommunication behavior of users. To weight the edges of CPG and UPG, we extract features for each pair of user and remote phone number in two different yet complementary aspects: 1) duration relatedness (DR) between user and phone number; and 2) frequency relatedness (FR) between user and phone number. Upon weighted CPG and UPG, we determine a trust value for each remote phone number. Finally, we conduct a comprehensive experimental study based on a dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate the effectiveness of our weighted HITS-based approach and show the strength of taking both DR and FR into account in feature extraction.