Web-based phishing attacks nowadays exploit popular cloud web hosting
services and apps such as Google Sites and Typeform for hosting their attacks.
Since these attacks originate from reputable domains and IP addresses of the
cloud services, traditional phishing detection methods such as IP reputation
monitoring and blacklisting are not very effective. Here we investigate the
effectiveness of deep learning models in detecting this class of cloud-based
phishing attacks. Specifically, we evaluate deep learning models for three
phishing detection methods--LSTM model for URL analysis, YOLOv2 model for logo
analysis, and triplet network model for visual similarity analysis. We train
the models using well-known datasets and test their performance on cloud-based
phishing attacks in the wild. Our results qualitatively explain why the models
succeed or fail. Furthermore, our results highlight how combining results from
the individual models can improve the effectiveness of detecting cloud-based
phishing attacks.

本文研究使用深度学习模型来检测利用 Google Sites 和 Typeform 等云服务进行的网络钓鱼攻击的有效性，包括 URL、商标和视觉相似性分析，证实了结合多种模型的结果可以提高检测云钓鱼攻击的有效性。