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DGA CapsNet: 1D Application of Capsule Networks to DGA Detection

Johns Hopkins University Applied Physics Laboratory (JHU/APL1), Laurel, MD 20723, USA
Information 2019, 10(5), 157; https://doi.org/10.3390/info10050157
Received: 26 February 2019 / Revised: 19 April 2019 / Accepted: 23 April 2019 / Published: 27 April 2019
(This article belongs to the Special Issue Machine Learning for Cyber-Security)
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Abstract

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. View Full-Text
Keywords: 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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Berman, D.S. DGA CapsNet: 1D Application of Capsule Networks to DGA Detection. Information 2019, 10, 157.

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