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Telecomm Fraud Detection via Attributed Bipartite Network

机译：通过属性双向网络进行电信欺诈检测

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摘要

Internet malicious incident occur frequently in recent years, bringing huge loss in different fields such as telecommunication, finance, etc. Such incidents generally entail fraudulent behaviors, which deviate from normal behavioral patterns. Particularly, in telecommunication, those anomalous interactions can be manifested by the network formed through interactions between callers and callees. Telecommunication network is indeed a bipartite, directed, and attributed network, and the anomalous network structure can be better captured by simultaneously considering these distinctive perspectives. To that end, in this paper, we propose a novel metric to measure the abnormality of dense subgraph structure by considering the structure, temporal changes, as well the attributes on nodes and edges. We further formulate an objective function and propose a greedy approach to discover the structure and corresponding fraudsters. The proposed metric and algorithm are experimented on a real-world telecommunication network dataset, which is shown to achieve competitive fraud detection performance than the baseline methods.
机译：近年来，Internet恶意事件频繁发生，给电信，金融等不同领域带来了巨大损失。此类事件通常带有欺诈行为，与正常的行为模式背道而驰。特别地，在电信中，那些异常的交互作用可以通过呼叫者和被叫者之间的交互作用形成的网络来体现。电信网络确实是一个双向的，定向的和归属的网络，并且可以通过同时考虑这些独特的观点来更好地捕获异常的网络结构。为此，在本文中，我们提出了一种新的度量，该度量通过考虑结构，时间变化以及节点和边的属性来测量稠密子图结构的异常。我们进一步制定了目标函数，并提出了一种贪婪的方法来发现结构和相应的欺诈者。在真实世界的电信网络数据集上对提出的度量和算法进行了实验，结果表明该方法和算法比基线方法具有竞争性的欺诈检测性能。

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