BROWSE
Detail
Journal Article
A Survey of Deep Learning-based Network Anomaly Detection
Cited 441 time in 
Download 23 time
Share 
Download 23 time
Share
- Authors
- Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, Kuinam J. Kim
- Issue Date
- 2019-01
- Citation
- Cluster Computing, v.22, no.Supp.1, pp.949-961
- ISSN
- 1386-7857
- Publisher
- Springer
- Language
- English
- Type
- Journal Article
- Project Code
- 17HH1900, Cloud based Security Intelligence Technology Development for the Customized Security Service Provisioning, Kim Jonghyun
- 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.
- KSP Keywords
- Deep belief network(DBN), Deep neural network(DNN), Learning approach, Learning-based, Machine Learning technique(MLT), Network anomaly detection, Network applications, Network traffic analysis, Recurrent Neural Network(RNN), deep learning(DL), enhanced performance