Attack Traffic Detection Based on LetNet-5 and GRU Hierarchical Deep Neural Network

被引：2
|
作者：
Wang, Zitian [1 ]
Wang, ZeSong [1 ]
Yi, FangZhou [1 ]
Zeng, Cheng [1 ,2 ,3 ]
机构：
[1] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China
[2] Hubei Prov Engn Technol Res Ctr Software Engn, Wuhan 430062, Peoples R China
[3] Hubei Engn Res Ctr Smart Govt & Artificial Intell, Wuhan 430062, Peoples R China
来源：
WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2021, PT III | 2021年 / 12939卷
基金：
中国国家自然科学基金;
关键词：
Attack traffic detection; Multi-dimensional layered network; CNN network; GRU network;
D O I：
10.1007/978-3-030-86137-7_36
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The paper converts the network traffic information about a single-channel grayscale image as input data. In addition, a deep hierarchical network model is designed, which combines LetNet-5 and GRU neural networks to analyze traffic data from both time and space dimensions. At the same time, two networks can be trained simultaneously to achieve better classification results because of the reasonable network association method. This paper uses the CICID2017 dataset, which contains multiple types of attacks and is time-sensitive. The experimental results show that, through the combination of deep neural networks, the model can classify attack traffic with extremely high accuracy.
引用
收藏
页码：327 / 334
页数：8
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