文章基本信息

标题：A Survey of Deep Learning Methods for Cyber Security
本地全文：下载
作者：Daniel S. Berman ; Anna L. Buczak ; Jeffrey S. Chavis 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：4
页码：122-156
DOI：10.3390/info10040122
出版社：MDPI Publishing
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.
关键词：cyber analytics; deep learning; deep neural networks; deep autoencoders; deep belief networks; restricted Boltzmann machines; convolutional neural networks cyber analytics ; deep learning ; deep neural networks ; deep autoencoders ; deep belief networks ; restricted Boltzmann machines ; convolutional neural networks