Research on Network Intrusion Detection Based on an Improved Deep Learning Method

Authors

  • Le Yang
  • Hua Chen

DOI:

https://doi.org/10.54097/ajst.v3i3.2553

Keywords:

Network security, Intrusion detection, Deep learning, Random forest.

Abstract

Network intrusion detection is an important research direction in the field of network security. The traditional detection algorithm is based on feature extraction and feature separation, which has the problems of low detection accuracy and high false alarm rate. In order to improve the accuracy of network intrusion detection, this paper proposes an intrusion detection model based on deep asymmetric convolutional encoder and Random Forest(RF). First, use DACAE to extract features from the preprocessed data, and then use the random forest algorithm to divide the network traffic data into normal and abnormal classes, and finally achieve the purpose of network intrusion detection. It is tested on three public benchmark datasets of network intrusion detection NSL-KDD and KDD99 datasets. The experimental results show that the accuracy and false alarm rate of the improved method are better than the comparative method.

References

Julisch, Klaus. Using root cause analysis to handle intrusion detection alarms. Diss. Universität Dortmund, 2003.

Zhang, Jiong, and Mohammad Zulkernine. "Anomaly based network intrusion detection with unsupervised outlier detection." 2006 IEEE International Conference on Communications. Vol. 5. IEEE, 2006.

Chen, Wun-Hwa, Sheng-Hsun Hsu, and Hwang-Pin Shen. "Application of SVM and ANN for intrusion detection." Computers & Operations Research 32.10 (2005): 2617-2634.

Gao, Ni, et al. "An intrusion detection model based on deep belief networks." 2014 Second international conference on advanced cloud and big data. IEEE, 2014.

R.Vinayakumar, K. P. Soman and P. Poornachandran, "Applying convolutional neural network for network intrusion detection," 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 1222-1228.

Shone, Nathan, et al. "A deep learning approach to network intrusion detection." IEEE transactions on emerging topics in computational intelligence 2.1 (2018): 41-50.

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Published

13 November 2022

How to Cite

Yang, L., & Chen, H. (2022). Research on Network Intrusion Detection Based on an Improved Deep Learning Method. Academic Journal of Science and Technology, 3(3), 73–76. https://doi.org/10.54097/ajst.v3i3.2553

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Section

Articles