DGA CapsNet: 1D Application of Capsule Networks to DGA Detection
AbstractDomain 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
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Berman, D.S. DGA CapsNet: 1D Application of Capsule Networks to DGA Detection. Information 2019, 10, 157.
Berman DS. DGA CapsNet: 1D Application of Capsule Networks to DGA Detection. Information. 2019; 10(5):157.Chicago/Turabian Style
Berman, Daniel S. 2019. "DGA CapsNet: 1D Application of Capsule Networks to DGA Detection." Information 10, no. 5: 157.
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