A Hybrid Methodologies for Intrusion Detection Based Deep Neural Network with Support Vector Machine and Clustering Technique

This paper proposes a novel approach called KDSVM, which utilized the k-mean techniques and advantage of feature learning with deep neural network (DNN) model and strong classifier of support vector machines (SVM) , to detection intrusion networks. KDSVM is composed of two stages. In the first step, the dataset is divided into k subset based on every sample distance by the cluster centers of k-means approach, and in the second step, testing dataset is distanced by the same cluster center and fed into the DNN model with SVM model for intrusion detection. The experimental results show that the KDSVM not only performs better than SVM, BPNN, DBN-SVM (Salama et al., Soft computing in industrial applications, 2011 [21]) and Bayes tree models in terms of detection accuracy and abnormal types of attacks found. It also provides an effective tool for the study and analysis of intrusion detection in the large network.

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