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An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network

This paper presents a network intrusion detection technique based on Synthetic Minority Over-Sampling Technique (SMOTE) and Deep Belief Network (DBN) applied to a class imbalance KDD-99 dataset. SMOTE is used to eliminate the class imbalance problem while intrusion classification is performed using...

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Main Authors: Adil, S.H., Ali, S.S.A., Raza, K., Hussaan, A.M.
Format: Article
Published: IOS Press 2014
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948783277&doi=10.3233%2f978-1-61499-434-3-94&partnerID=40&md5=95f8ccf40d3162ffa742623976dd0f66
http://eprints.utp.edu.my/31728/
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spelling my.utp.eprints.317282022-03-29T03:36:10Z An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network Adil, S.H. Ali, S.S.A. Raza, K. Hussaan, A.M. This paper presents a network intrusion detection technique based on Synthetic Minority Over-Sampling Technique (SMOTE) and Deep Belief Network (DBN) applied to a class imbalance KDD-99 dataset. SMOTE is used to eliminate the class imbalance problem while intrusion classification is performed using DBN. The proposed technique first resolves the class imbalance problem in the KDD-99 dataset followed by DBN to estimate the initial model. The accuracy is further enhanced by using multilayer perceptron networks. The obtained results are compared with the existing best technique based on reduced size recurrent neural network. The study shows that our approach is competitive and efficient in classifying both intrusion and normal patterns in KDD-99 dataset. © 2014 The authors and IOS Press. All rights reserved. IOS Press 2014 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948783277&doi=10.3233%2f978-1-61499-434-3-94&partnerID=40&md5=95f8ccf40d3162ffa742623976dd0f66 Adil, S.H. and Ali, S.S.A. and Raza, K. and Hussaan, A.M. (2014) An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network. Frontiers in Artificial Intelligence and Applications, 265 . pp. 94-102. http://eprints.utp.edu.my/31728/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description This paper presents a network intrusion detection technique based on Synthetic Minority Over-Sampling Technique (SMOTE) and Deep Belief Network (DBN) applied to a class imbalance KDD-99 dataset. SMOTE is used to eliminate the class imbalance problem while intrusion classification is performed using DBN. The proposed technique first resolves the class imbalance problem in the KDD-99 dataset followed by DBN to estimate the initial model. The accuracy is further enhanced by using multilayer perceptron networks. The obtained results are compared with the existing best technique based on reduced size recurrent neural network. The study shows that our approach is competitive and efficient in classifying both intrusion and normal patterns in KDD-99 dataset. © 2014 The authors and IOS Press. All rights reserved.
format Article
author Adil, S.H.
Ali, S.S.A.
Raza, K.
Hussaan, A.M.
spellingShingle Adil, S.H.
Ali, S.S.A.
Raza, K.
Hussaan, A.M.
An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
author_facet Adil, S.H.
Ali, S.S.A.
Raza, K.
Hussaan, A.M.
author_sort Adil, S.H.
title An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
title_short An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
title_full An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
title_fullStr An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
title_full_unstemmed An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
title_sort improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
publisher IOS Press
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948783277&doi=10.3233%2f978-1-61499-434-3-94&partnerID=40&md5=95f8ccf40d3162ffa742623976dd0f66
http://eprints.utp.edu.my/31728/
_version_ 1738657288201175040
score 13.160551

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