A Survey of Deep Learning Methods for Cyber Security

被引：274

作者：

Berman, Daniel S. ^{[1
]}

Buczak, Anna L. ^{[1
]}

Chavis, Jeffrey S. ^{[1
]}

Corbett, Cherita L. ^{[1
]}

机构：

[1] JHU, Appl Phys Lab, APL1, Laurel, MD 20910 USA

来源：

INFORMATION | 2019年 / 10卷 / 04期

关键词：

cyber analytics; deep learning; deep neural networks; deep autoencoders; deep belief networks; restricted Boltzmann machines; convolutional neural networks; ANDROID MALWARE CHARACTERIZATION; NETWORK INTRUSION DETECTION; ATTACK DETECTION; BELIEF NETWORKS; NEURAL-NETWORKS; REPRESENTATIONS; CLASSIFICATION; INTERNET;

D O I：

10.3390/info10040122

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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页数：35

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