Research on Intrusion Detection Technology Based on Densely Connected Convolutional Neural Networks

Xianghua MIAO; Xiaoche SHAN

doi:10.11999/JEIT190655

Volume 42 Issue 11

Nov. 2020

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Xianghua MIAO, Xiaoche SHAN. Research on Intrusion Detection Technology Based on Densely Connected Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2706-2712. doi: 10.11999/JEIT190655

Citation:

Xianghua MIAO, Xiaoche SHAN. Research on Intrusion Detection Technology Based on Densely Connected Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2706-2712. doi: 10.11999/JEIT190655

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Research on Intrusion Detection Technology Based on Densely Connected Convolutional Neural Networks

doi: 10.11999/JEIT190655

Xianghua MIAO,
Xiaoche SHAN^,

School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

Received Date: 2019-08-29
Rev Recd Date: 2020-05-09

Available Online: 2020-05-28

Publish Date: 2020-11-16

Abstract

Abstract

Convolutional Neural Network (CNN) is widely used in the field of intrusion detection technology. It is generally believed that the deeper the network structure, the more accurate in feature extraction and detection accuracy. However, it is accompanied with the problems of gradient dispersion, insufficient generalization ability and low accuracy of parameters. In view of the above problems, the Densely Connected Convolutional Network (DCCNet) is applied into the intrusion detection technology, and achieve the purpose of improving the detection accuracy by using the hybrid loss function. Experiments are performed with the KDD 99 data set, and the experimental results are compared with the commonly used LeNet neural network and VggNet neural network structure. Finally, the analysis shows that the accuracy of detection is improved, and the problem of gradient vanishing during training is alleviated.
- Intrusion detection,
- Convolutional Neural Network (CNN),
- Dense connection,
- Gradient vanishing

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References(17)

References

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