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Citation: Hongsong CHEN, Jingjiu CHEN. Recurrent Neural Networks Based Wireless Network Intrusion Detection and Classification Model Construction and Optimization[J]. Journal of Electronics and Information Technology, ;2019, 41(6): 1427-1433. doi: 10.11999/JEIT180691 shu

Recurrent Neural Networks Based Wireless Network Intrusion Detection and Classification Model Construction and Optimization

  • Corresponding author: Hongsong CHEN, chenhs@ustb.edu.cn
  • Received Date: 2018-07-10
    Accepted Date: 2019-01-07
    Available Online: 2019-06-01

Figures(5) / Tables(8)

  • In order to improve the comprehensive performance of the wireless network intrusion detection model, Recurrent Neural Network (RNN) algorithm is used to build a wireless network intrusion detection classification model. For the over-fitting problem of the classification model caused by the imbalance of training data samples distribution in wireless network intrusion detection, based on the pre-treatment of raw data cleaning, transformation, feature selection, etc., an instance selection algorithm based on window is proposed to refine the train data-set. The network structure, activation function and re-usability of the attack classification model are optimized experimentally, so the optimization model is obtained finally. The classification accuracy of the optimization model is 98.6699%, and the running time after the model reuse optimization is 9.13 s. Compared to other machine learning algorithms, the proposed approach achieves good results in classification accuracy and execution efficiency. The comprehensive performances of the proposed model are better than those of traditional intrusion detection model.
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