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Deep Learning in Cybersecurity: Challenges and Approaches

Deep Learning in Cybersecurity: Challenges and Approaches

Yadigar N. Imamverdiyev, Fargana J. Abdullayeva
Copyright: © 2020 |Volume: 10 |Issue: 2 |Pages: 24
ISSN: 1947-3435|EISSN: 1947-3443|EISBN13: 9781799807124|DOI: 10.4018/IJCWT.2020040105
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MLA

Imamverdiyev, Yadigar N., and Fargana J. Abdullayeva. "Deep Learning in Cybersecurity: Challenges and Approaches." IJCWT vol.10, no.2 2020: pp.82-105. http://doi.org/10.4018/IJCWT.2020040105

APA

Imamverdiyev, Y. N. & Abdullayeva, F. J. (2020). Deep Learning in Cybersecurity: Challenges and Approaches. International Journal of Cyber Warfare and Terrorism (IJCWT), 10(2), 82-105. http://doi.org/10.4018/IJCWT.2020040105

Chicago

Imamverdiyev, Yadigar N., and Fargana J. Abdullayeva. "Deep Learning in Cybersecurity: Challenges and Approaches," International Journal of Cyber Warfare and Terrorism (IJCWT) 10, no.2: 82-105. http://doi.org/10.4018/IJCWT.2020040105

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Abstract

In this article, a review and summarization of the emerging scientific approaches of deep learning (DL) on cybersecurity are provided, a structured and comprehensive overview of the various cyberattack detection methods is conducted, existing cyberattack detection methods based on DL is categorized. Methods covering attacks to deep learning based on generative adversarial networks (GAN) are investigated. The datasets used for the evaluation of the efficiency proposed by researchers for cyberattack detection methods are discussed. The statistical analysis of papers published on cybersecurity with the application of DL over the years is conducted. Existing commercial cybersecurity solutions developed on deep learning are described.

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