Network intrusion detection method based on deep learning feature extraction

被引：0
|
作者：
Song Y. [1 ,2 ]
Hou B. [1 ]
Cai Z. [1 ]
机构：
[1] College of Computer, National University of Defense Technology, Changsha
[2] Department of Engineering Technology, Hunan Vocational College for Nationalities, Yueyang
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2021年 / 49卷 / 02期
关键词：
Deep learning; Feature extraction; Inhibition and stimulation; Layer by layer greedy training; Sparse self-coding; Support vector machine;
D O I：
10.13245/j.hust.210219
中图分类号：
学科分类号：
摘要：
For the construction of deep learning model, the number of hidden layers and the number of neuron nodes in each layer of neural network were set by artificial expert's subjective experience, the deep learning model was not intelligent and adaptable, so a kind of applied to network intrusion detection deep learning of adaptive and intelligent feature extraction method was put forward.A method that the strategy of greed training layer by layer was adopted, and an adaptive and intelligent feature extraction neural network by improving the training method of sparse self-coding neural network was formed.In the end, a network intrusion detection system based on deep learning feature extraction was developed by using a multi-class classifier based on support vector machine.The experiment shows that proposed method compared with support vector machine based on autoencoder network (AN-SVM) and support vector machine model combining kernel principal component analysis with genetic algorithm (KPA-GA-SVM) methods, the average accuracy is increased by 5.01%, the false alarm rate is reduced by 6.24%, and the detection time is reduced by 16%, it shows that this method is superior to other similar methods. © 2021, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：115 / 120
页数：5
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