%0 Journal Article
%@ 09226389
%A Adil, S.H.
%A Ali, S.S.A.
%A Raza, K.
%A Hussaan, A.M.
%D 2014
%F scholars:4523
%I IOS Press
%J Frontiers in Artificial Intelligence and Applications
%K Classification (of information); Mercury (metal); Recurrent neural networks, Class imbalance problems; DBN; Deep belief network (DBN); Intrusion detection approaches; Multi layer perceptron; Multi-layer perceptron networks; Network intrusion detection; Synthetic minority over-sampling techniques, Intrusion detection
%P 94-102
%R 10.3233/978-1-61499-434-3-94
%T An improved intrusion detection approach using synthetic minority over-sampling technique and deep belief network
%U https://khub.utp.edu.my/scholars/4523/
%V 265
%X 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.
%Z cited By 9; Conference of 13th International Conference on New Trends in Intelligent Software Methodology Tools, and Techniques, SoMeT 2014 ; Conference Date: 22 September 2014 Through 24 September 2014; Conference Code:116901