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

Hongsong CHEN; Jingjiu CHEN

doi:10.11999/JEIT180691

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1427-1433

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

Citation:

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

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

doi: 10.11999/JEIT180691

Hongsong CHEN^{1, 2
,
,},
Jingjiu CHEN¹

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China

Funds: The National Key Research Development Program (2018YFB0803400, 2018YFB0803403), The National Social Science Foundation of China (18BGJ071)

Received Date: 2018-07-10
Rev Recd Date: 2019-01-07

Available Online: 2019-01-18

Publish Date: 2019-06-01

Abstract

Abstract

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.
- Intrusion detection,
- Recurrent Neural Network (RNN),
- Instance selection,
- Model optimization,
- experimental verification

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References(12)

References

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