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

Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models