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Cluster Computing
Erschienen in: Cluster Computing 1/2019

27.09.2017

A survey of deep learning-based network anomaly detection

verfasst von: Donghwoon Kwon, Hyunjoo Kim, Jinoh Kim, Sang C. Suh, Ikkyun Kim, Kuinam J. Kim

Erschienen in: Cluster Computing | Sonderheft 1/2019

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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.
Vorheriger Artikel Prototype implementation of the OpenGL ES 2.0 shading language offline compiler
Nächster Artikel Testing the consistency of business data objects using extended static testing of CRUD matrices

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Metadaten
Titel
A survey of deep learning-based network anomaly detection
verfasst von
Donghwoon Kwon
Hyunjoo Kim
Jinoh Kim
Sang C. Suh
Ikkyun Kim
Kuinam J. Kim
Publikationsdatum
27.09.2017
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 1/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1117-8

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