文章基本信息

标题：DGA CapsNet: 1D Application of Capsule Networks to DGA Detection
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作者：Daniel S. Berman
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：5
页码：1-15
DOI：10.3390/info10050157
出版社：MDPI Publishing
摘要：Domain generation algorithms (DGAs) represent a class of malware used to generate large numbers of new domain names to achieve command-and-control (C2) communication between the malware program and its C2 server to avoid detection by cybersecurity measures. Deep learning has proven successful in serving as a mechanism to implement real-time DGA detection, specifically through the use of recurrent neural networks (RNNs) and convolutional neural networks (CNNs). This paper compares several state-of-the-art deep-learning implementations of DGA detection found in the literature with two novel models: a deeper CNN model and a one-dimensional (1D) Capsule Networks (CapsNet) model. The comparison shows that the 1D CapsNet model performs as well as the best-performing model from the literature.
关键词：deep learning; deep neural networks; capsule networks; convolutional neural networks; cybersecurity; domain generation algorithms deep learning ; deep neural networks ; capsule networks ; convolutional neural networks ; cybersecurity ; domain generation algorithms