Deep Learning in Cybersecurity: Challenges and Approaches

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Author(s): Yadigar N. Imamverdiyev (Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan)and Fargana J. Abdullayeva (Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku, Azerbaijan)
Copyright: 2021
Pages: 29
Source title: Research Anthology on Artificial Intelligence Applications in Security
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-7705-9.ch095

Keywords: Artificial Intelligence / Computer Science & IT / Cyber and Network Security / Information Science Reference

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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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Deep Learning in Cybersecurity: Challenges and Approaches

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