A Survey of Deep Learning Methods for Cyber Security release_svpkppmsljdavgzbz4oq55shbm

by Daniel Berman, Anna Buczak, Jeffrey Chavis, Cherita Corbett

Published in Information by MDPI AG.

2019   Volume 10, p122

Abstract

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.
In application/xml+jats format

Archived Files and Locations

application/pdf  3.0 MB
file_v6wwtek5erbppixfbncwvacw4q
web.archive.org (webarchive)
res.mdpi.com (web)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2019-04-02
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2078-2489
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 28c89fc1-1c2c-4395-9217-b98855b2e836
API URL: JSON