Deep Learning in Cybersecurity-Reference-Cited by-同舟云学术

Deep Learning in Cybersecurity

Published:2021 Issue: Volume: Page:2164-2192
ISSN:
Container-title:Research Anthology on Artificial Intelligence Applications in Security
language:
Short-container-title:

Author:

Imamverdiyev Yadigar N.¹^ORCID,Abdullayeva Fargana J.²^ORCID

Affiliation:

1. Institute of Information Technology, Azerbaijan National Academy of Sciences, Baku, Azerbaijan

2. Institute of Information Technology of Azerbaijan National Academy of Sciences, Baku, Azerbaijan

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.

Publisher

IGI Global

Reference84 articles.

1. Aditham, S., Ranganathan, N., & Katkoori, S. (2017). LSTM-based memory profiling for predicting data attacks in distributed Big Data systems. In Proccedings of the IEEE international parallel and distributed processing symposium workshops (pp. 1259-1267). IEEE Press.

2. AL-Hawawreh, M., Moustafa, N., & Sitnikova, E. (2018). Identification of malicious activities in industrial internet of things based on deep learning models. Journal of information security and applications, 41, 1-11.

3. Hybridisation of classifiers for anomaly detection in big data

4. Almeida, D. M. (2016). Malware classification on time series data through machine learning [thesis]. Faculdade de Engenharia da Universidade Do Porto.

5. Intrusion detection using deep belief networks