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DESNN Algorithm for Communication Network Intrusion Detection

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Abstract

Intrusion detection is a crucial technology in the communication network security field. In this paper, a dynamic evolutionary sparse neural network (DESNN) is proposed for intrusion detection, named as DESNN algorithm. Firstly, an ensemble neural network model is constructed, which is processed by a dynamic pruning rule and further divided into advantage subnetworks and disadvantage subnetworks. The dynamic pruning rule can effectively reduce the subnetworks weight parameters, thereby increasing the speed of the subnetworks intrusion detection. Then considering the subnetworks performance loss caused by the dynamic pruning rule, a novel evolutionary mechanism is proposed to optimize the training process of the disadvantage subnetworks. The weight of the disadvantage subnetworks approach the weight of the advantage subnetworks by the evolutionary mechanism, such that the performance of the ensemble neural network can be improved. Finally, an optimal subnetwork is selected from the ensemble neural network, which is used to detect multiple types of intrusion. Experiments show that the proposed DESNN algorithm improves intrusion detection speed without causing significant performance loss compare with other fully-connected neural network models.

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Data Availability

The KDD-Cup 99 data in this paper are from its official website as http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No.61971117, by the Natural Science Foundation of Hebei Province (Grant No. F2020501007).

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The authors have not disclosed any funding.

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Author notes

  1. Fulai Liu and Lijie Zhang have contributed equally to this study.

Authors and Affiliations

  1. Engineer Optimization & Smart Antenna Institute, Northeastern University at Qinhuangdao, Qinhuangdao, China

    Fulai Liu & Ruiyan Du

  2. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Fulai Liu, Jialiang Xu, Lijie Zhang, Ruiyan Du, Zhibo Su, Aiyi Zhang & Zhongyi Hu

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  1. Fulai Liu
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  2. Jialiang Xu
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  3. Lijie Zhang
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  4. Ruiyan Du
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  6. Aiyi Zhang
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Correspondence to Fulai Liu or Lijie Zhang.

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Cite this article

Liu, F., Xu, J., Zhang, L. et al. DESNN Algorithm for Communication Network Intrusion Detection. Wireless Pers Commun 126, 1705–1720 (2022). https://doi.org/10.1007/s11277-022-09817-5

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  • DOI: https://doi.org/10.1007/s11277-022-09817-5

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