With the development of Internet technology, botnets have become a major threat to most of the computers over the Internet. Most sophisticated bots use Domain Generation Algorithms (DGAs) to automatically generate a large number of pseudo-random domain names in Domain Name Service (DNS) domain fluxing, which can allow malware to communicate with Command and Control (C&C) server. To cope with this challenge, we built a novel Two-Stream network-based deep learning framework (named TS-ASRCaps) that uses multimodal information to reflect the properties of DGAs. Furthermore, we proposed an Attention Sliced Recurrent Neural Network (ATTSRNN) to automatically mine the underlying semantics. We also used a Capsule Network (CapsNet) with dynamic routing to model high-level visual information. Finally, we emphasized how the multimodal-based model outperforms other state-of-the-art models for the classification of domain names. To the best of our knowledge, this is the first work that the multimodal deep learning have been empirically investigated for DGA botnet detection.
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The authors would like to thank the Editor-in-Chief, the Associate Editor, and the reviewers for their insightful comments and suggestions. This work was supported by the Research Innovation Project of Graduate Student in Xinjiang Uygur Autonomous Region (XJ2019G065), the CERNET Innovation Project (NGII20170420, NGII20190412) and the Xinjiang Uygur Autonomous Region Cyber Security and Informatization Project (XJWX-1-Z-2019-1021).
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Pei, X., Tian, S., Yu, L. et al. A Two-Stream Network Based on Capsule Networks and Sliced Recurrent Neural Networks for DGA Botnet Detection. J Netw Syst Manage 28, 1694–1721 (2020). https://doi.org/10.1007/s10922-020-09554-9
- Two-stream network
- Capsule network
- Sliced recurrent neural network
- Domain Generation Algorithms