Method for assessment of network security situation with deep learning

被引：0

作者：

Yang H. ^{[1
]}

Zeng R. ^{[1
]}

机构：

[1] School of Computer Science and Technology, Civil Aviation University of China, Tianjin

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2021年 / 48卷 / 01期

关键词：

Data resampling; Deep autoencoder; Deep learning; Network attacks; Network security situation assessment;

D O I：

10.19665/j.issn1001-2400.2021.01.021

中图分类号：

学科分类号：

摘要：

The traditional methods for assessment of network security situation rely on manual label and evaluation.When faced with a large amount of data,there appearsome problems such as low efficiency and poor flexibility.First,we propose a Deep Autoencoder-Deep Neural Network (DAEDNN) model to identify all kinds of attacks on the network.Then,the Under-Over Sampling Weighted (UOSW) algorithm is designed to improve the detection rate of the model on categories with a few training samples.Finally,we conduct model testing and calculate the attack probability.Besides,we determine the impact score of each type of attack and calculate the network security situation value.Experimental results show that the precision and recall of the proposed model are better than those of the compared models,and that the proposed model has a better performance in accuracy and efficiency. © 2021, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：183 / 190

页数：7

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