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- W3174191604 abstract "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." @default.
- W3174191604 created "2021-07-05" @default.
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- W3174191604 date "2021-08-26" @default.
- W3174191604 modified "2023-10-16" @default.
- W3174191604 title "Deep learning algorithms for cyber security applications: A survey" @default.
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- W3174191604 cites W2082550445 @default.
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- W3174191604 cites W2399941526 @default.
- W3174191604 cites W2410828832 @default.
- W3174191604 cites W2414564754 @default.
- W3174191604 cites W2508015754 @default.
- W3174191604 cites W2516866958 @default.
- W3174191604 cites W2549585799 @default.
- W3174191604 cites W2550999023 @default.
- W3174191604 cites W2554148185 @default.
- W3174191604 cites W2574852830 @default.
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- W3174191604 doi "https://doi.org/10.3233/jcs-200095" @default.
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