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Open AccessArticle

DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism

1
Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen 518000, China
2
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150000, China
3
School of Computer Science and Engineering, VIT University, Vellore 632014, India
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4205; https://doi.org/10.3390/app9204205
Received: 15 September 2019 / Revised: 30 September 2019 / Accepted: 3 October 2019 / Published: 9 October 2019
Currently, many cyberattacks use the Domain Generation Algorithm (DGA) to generate random domain names, so as to maintain communication with the Communication and Control (C&C) server. Discovering DGA domain names in advance could help to detect attacks and response in time. However, in recent years, the General Data Protection Regulation (GDPR) has been promulgated and implemented, and the method of DGA classification based on the context information, such as the WHOIS (the information about the registered users or assignees of the domain name), is no longer applicable. At the same time, acquiring the DGA algorithm by reversing malware samples encounters the problem of no malware samples for various reasons, such as fileless malware. We propose a DGA domain name classification method based on Long Short-Term Memory (LSTM) with attention mechanism. This method is oriented to the character sequence of the domain name, and it uses the LSTM combined with attention mechanism to construct the DGA domain name classifier to achieve the rapid classification of domain names. The experimental results show that the method has a good classification result. View Full-Text
Keywords: security; DGA classification; attention mechanism; LSTM security; DGA classification; attention mechanism; LSTM
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MDPI and ACS Style

Qiao, Y.; Zhang, B.; Zhang, W.; Sangaiah, A.K.; Wu, H. DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism. Appl. Sci. 2019, 9, 4205.

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