Given a stream of financial transactions between traders in an e-market, how can we accurately detect fraudulent traders and suspicious behaviors in real time? Despite the efforts made in detecting these fraudsters, this field still faces serious challenges, including the ineffectiveness of existing methods for the complex and streaming environment of e-markets. As a result, it is still difficult to quickly and accurately detect suspected traders and behavior patterns in real-time transactions, and it is still considered an open problem. To solve this problem and alleviate the existing challenges, in this article, we propose FiFrauD, which is an unsupervised, scalable approach that depicts the behavior of manipulators in a transaction stream. In this approach, real-time transactions between traders are converted into a stream of graphs and, instead of using supervised and semi-supervised learning methods, fraudulent traders are detected precisely by exploiting density signals in graphs. Specifically, we reveal the traits of fraudulent traders in the market and propose a novel metric from this perspective, i.e., graph topology, time, and behavior. Then, we search for suspicious blocks by greedily optimizing the proposed metric. Theoretical analysis demonstrates upper bounds for FiFrauD's effectiveness in catching suspicious trades. Extensive experiments on five real-world datasets with both actual and synthetic labels demonstrate that FiFrauD achieves significant accuracy improvements compared with state-of-the-art fraud detection methods. Also, it can find various suspicious behavior patterns in a linear runtime and provide interpretable results. Furthermore, FiFrauD is resistant to the camouflage tactics used by fraudulent traders.

考虑到电子市场中交易者之间的金融交易流，我们如何实时准确地检测欺诈交易者和可疑行为？尽管在检测这些欺诈者方面做出了努力，但该领域仍然面临着严峻的挑战，包括现有方法对于电子市场复杂和流媒体环境的无效性。因此，在实时交易中快速准确地检测可疑交易者和行为模式仍然很困难，仍然被认为是一个悬而未决的问题。为了解决这个问题并缓解现有的挑战，在本文中，我们提出了 FiFrauD，这是一种无监督的、可扩展的方法，描述了交易流中操纵者的行为。在这种方法中，交易者之间的实时交易被转换为图形流，并且不使用监督和半监督学习方法，而是通过利用图形中的密度信号来精确检测欺诈交易者。具体来说，我们揭示了市场上欺诈交易者的特征，并从这个角度提出了一种新颖的指标，即图拓扑、时间和行为。然后，我们通过贪婪地优化建议的指标来搜索可疑块。理论分析表明 FiFrauD 在捕捉可疑交易方面的有效性存在上限。对五个具有实际标签和合成标签的真实数据集进行的广泛实验表明，与最先进的欺诈检测方法相比，FiFrauD 实现了显着的准确性改进。此外，它还可以在线性运行时发现各种可疑行为模式并提供可解释的结果。此外，FiFrauD 能够抵御欺诈交易者使用的伪装策略。