A Survey of Deep Learning Methods for Cyber Security
AbstractThis 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
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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.
Berman DS, Buczak AL, Chavis JS, Corbett CL. A Survey of Deep Learning Methods for Cyber Security. Information. 2019; 10(4):122.Chicago/Turabian Style
Berman, Daniel S.; Buczak, Anna L.; Chavis, Jeffrey S.; Corbett, Cherita L. 2019. "A Survey of Deep Learning Methods for Cyber Security." Information 10, no. 4: 122.
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