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Information 2019, 10(4), 122; https://doi.org/10.3390/info10040122

A Survey of Deep Learning Methods for Cyber Security

Johns Hopkins University Applied Physics Laboratory (JHU/APL1), Laurel, MD 20910, USA
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Received: 14 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 2 April 2019
(This article belongs to the Special Issue Machine Learning for Cyber-Security)
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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. View Full-Text
Keywords: 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
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Berman, D.S.; Buczak, A.L.; Chavis, J.S.; Corbett, C.L. A Survey of Deep Learning Methods for Cyber Security. Information 2019, 10, 122.

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