Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.

中文翻译：

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通过深度学习自动进行欺诈性电话识别

多项研究表明，电话产生的电话号码和通话行为揭示了电话的类型。通过分析电话号码规则和呼叫行为模式，我们可以识别欺诈性电话。这种识别的成功在很大程度上取决于用于构造分类器的特定功能集。由于这些功能是人工设计的，因此对电话欺诈的任何更改都会使这些精心构建的功能无效。在本文中，我们证明了我们可以通过应用我们提出的基于深度学习的新颖方法，自动执行要素工程流程，从而自动识别欺诈性电话。我们设计并构造了基于呼叫详细记录（CDR）的新分类器，用于欺诈性电话识别，发现基于深度学习的方法所实现的性能优于竞争方法。实验结果证明了该方法的有效性。具体而言，在我们的准确性评估中，获得的准确性超过99％，并且性能最高的深度学习模型的平均准确性比最新的识别模型高4.7％。此外，我们证明了深度学习方法在现实环境中非常稳定，并且通过这种方法自动学习的隐式功能对于欺诈性电话号码及其呼叫行为随时间的动态变化具有更大的弹性。