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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IOS Press
2014
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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/ |
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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/ |
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