论文标题
通过结合深度学习模型来检测基于云的网络钓鱼攻击
Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models
论文作者
论文摘要
如今，基于Web的网络钓鱼攻击可利用流行的云网络托管服务和Google网站等应用程序和用于托管攻击的类型。由于这些攻击源自云服务的信誉良好的域和IP地址，因此传统的网络钓鱼检测方法（例如IP声誉监视和黑名单）不是很有效。在这里，我们研究了深度学习模型在检测这类基于云的网络钓鱼攻击方面的有效性。具体而言，我们评估了三种网络钓鱼检测方法的深度学习模型 - 用于URL分析的LSTM模型，用于徽标分析的YOLOV2模型以及用于视觉相似性分析的三重态网络模型。我们使用著名的数据集训练模型，并在野外基于云的网络钓鱼攻击上测试其性能。我们的结果定性地解释了为什么模型成功或失败。此外，我们的结果突出了各个模型的结果如何提高检测基于云的网络钓鱼攻击的有效性。
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.
