Comparative study of CNN and RNN for deep learning based intrusion detection system

Cui, J; Long, J; Min, E; Liu, Q; Li, Q

Comparative study of CNN and RNN for deep learning based intrusion detection system

Cui, J Long, J Min, E Liu, Q Li, Q

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11067 LNCS pp. 159 - 170
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Cui, J	en_US
dc.contributor.author	Long, J	en_US
dc.contributor.author	Min, E	en_US
dc.contributor.author	Liu, Q	en_US
dc.contributor.author	Li, Q https://orcid.org/0000-0002-6149-5081	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11067 LNCS pp. 159 - 170	en_US
dc.identifier.isbn	9783030000172	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128696
dc.description.abstract	© Springer Nature Switzerland AG 2018. Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-00018-9_15	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Comparative study of CNN and RNN for deep learning based intrusion detection system	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11067 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.declined	2018-11-27T16:57:16.337+1100
pubs.publication-status	Published	en_US
pubs.volume	11067 LNCS	en_US

Abstract:

© Springer Nature Switzerland AG 2018. Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.

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http://hdl.handle.net/10453/128696