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Network Intrusion Detection Based on Neural Networks and D-S Evidence

  • Chunlin Lu
  • Lidong ZhaiEmail author
  • Tao Liu
  • Na Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9555)

Abstract

Network traffic data is an important source of data to establish a network intrusion detection system (NIDS). The explosive growth of the network traffic data brings a huge challenge to network intrusion detection, and video traffic packet has been an important part of the network traffic. In recent years, more and more researches have been applied Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, to improve the performance of intrusion detection systems. However, in view of the current network intrusion detection methods, the detection precision, especially for low-frequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on BP neural network that is optimized by genetic algorithm and Dempster-Shafer (D-S) theory to solve the above problems and help NIDS to achieve higher detection rate, less false positive rate and stronger stability. The general process of our model is as follows: firstly dividing the main extracted feature into several different feature subsets. Then, based on different feature subsets, different ANN models are trained to build the detection engine. Finally, the D-S evidence theory is employed to integration these results,and obtain the final result. The effectiveness of this method is verified by experimental simulation utilizing KDD Cup1999 dataset.

Keywords

Network intrusion detection BP neural network Dempster shafer Anomaly detection 

Notes

Acknowledgment

This work is partially supported by the Fundamental Research Funds for the Central Universities under Grants No. 2014MS99 and National Natural Science Foundation of China under Grants No. 61302105.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.North China Electric Power UniversityBeijingChina
  2. 2.Institute of Computing TechnologyChinese Academy of SciencesBeijingChina
  3. 3.Institute of China Mobile Communication Company LimitedBeijingChina

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