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Cluster Computing

, Volume 22, Supplement 1, pp 949–961 | Cite as

A survey of deep learning-based network anomaly detection

  • Donghwoon Kwon
  • Hyunjoo KimEmail author
  • Jinoh Kim
  • Sang C. Suh
  • Ikkyun Kim
  • Kuinam J. KimEmail author
Article

Abstract

A great deal of attention has been given to deep learning over the past several years, and new deep learning techniques are emerging with improved functionality. Many computer and network applications actively utilize such deep learning algorithms and report enhanced performance through them. In this study, we present an overview of deep learning methodologies, including restricted Bolzmann machine-based deep belief network, deep neural network, and recurrent neural network, as well as the machine learning techniques relevant to network anomaly detection. In addition, this article introduces the latest work that employed deep learning techniques with the focus on network anomaly detection through the extensive literature survey. We also discuss our local experiments showing the feasibility of the deep learning approach to network traffic analysis.

Keywords

Network anomaly detection Deep learning Network traffic analysis Intrusion detection Network security 

Notes

Acknowledgements

The authors are grateful to Ritesh Malaiya for his assistance for experimenting. This work was supported in part by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. 2016-0-00078, Cloud-based Security Intelligence Technology Development for the Customized Security Service Provisioning).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Computer Science and Information SystemsTexas A&M University, CommerceCommerceUSA
  2. 2.Information Security Research DivisionElectronics & Telecommunications Research InstituteDaejeonKorea
  3. 3.Department of Convergence SecurityKyonggi UniversitySuwonKorea

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