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

  • Tao Ma
  • Yang Yu
  • Fen Wang
  • Qiang Zhang
  • Xiaoyun Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

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.

Keywords

Intrusion detection systems Deep neural network Hybrid model K-means clustering Support vector machine 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 11361046) and the Key Research Fund of Ningxia Normal University (Grant No. NXSFZD1517 NXSFZD1603 and NXSFZD1608), the Natural Science Fund of Ningxia Province (Grant NZ16260) and the Fundamental Research Fund for Senior School of Ningxia Province (Grant No. NGY2015124).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Mathematical and Computer ScienceNingxia Normal UniversityGuyuanChina
  3. 3.Statistics & Research DivisionChina Insurance Regulatory Commission Ningxia BureauYinchuanChina

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