Survey of Network Attack Detection Based on GAN

doi:10.3969/j.issn.1671-1122.2019.02.001

Abstract

Abstract:

Generative adversarial network (GAN) is a major breakthrough in the field of deep learning in recent years. It is a dynamic game model composed of generator and discriminator. Its ideas of “generation” and “confrontation” have won the favor of the vast number of scientific researchers and met the application needs of many research fields. Inspired by the ideas, researchers applied GAN to the field of network security to detect network attacks and help build an intelligent and effective network security protection mechanism. This paper introduces the basic principle, infrastructure, theoretical development and application status of GAN, and focuses on the application status of GAN in the field of network attack detection from two aspects of network attack sample generation and network attack behavior detection.

Key words: GAN, generator, discriminator, network attack, network security

CLC Number:

TP309

Jianming FU, Lin LI, Rui ZHENG, Suriguga. Survey of Network Attack Detection Based on GAN[J]. Netinfo Security, 2019, 19(2): 1-9.

Figures/Tables 7

References 33

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项目模型	特征选择	训练数据有无标签	检测结果输出	应用领域
t-GAN	无需人工选取	无	二分类（正常/恶意）	无监督学习
t-DCGAN	无需人工选取	无	二分类（正常/恶意）	无监督学习
Bot-GAN	人工选取	无	三分类（正常/ 异常/虚假）	无监督学习
CF-GAN	无需人工选取	有	二分类（合法/违法）	有监督学习
GAN-DAE	无需人工选取	有	三分类（正常/ 欺诈/未知）	有监督学习