Deep learning algorithms for cyber security applications: A survey

Li, Guangjun, Sharma, Preetal, Pan, Lei, Rajasegarar, Sutharshan, Karmakar, Chandan and Patterson, Nicholas 2021, Deep learning algorithms for cyber security applications: A survey, Journal of Computer Security, pp. 1-25, doi: 10.3233/jcs-200095.

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Title Deep learning algorithms for cyber security applications: A survey
Author(s) Li, Guangjun
Sharma, Preetal
Pan, LeiORCID iD for Pan, Lei
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan
Karmakar, ChandanORCID iD for Karmakar, Chandan
Patterson, NicholasORCID iD for Patterson, Nicholas
Journal name Journal of Computer Security
Start page 1
End page 25
Total pages 25
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2021-06-18
ISSN 0926-227X
Keyword(s) Deep learning
cyber security
malware detection
intrusion detection
privacy breaches
Summary With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.
Notes In Press
Language eng
DOI 10.3233/jcs-200095
Indigenous content off
Field of Research 0803 Computer Software
HERDC Research category C1 Refereed article in a scholarly journal
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