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Abstract

According to the numerous present-day requirements in computer security environments, this paper gives an overview of cybersecurity from the standpoint of neural networks and deep learning algorithms. It covers how these techniques can be used in a variety of cybersecurity tasks, including intrusion detection, malware or botnet identification, phishing, cyber attack prediction, denial of service, and cyber anomalies, among others. The analytical-synthetic method was used for this investigation to find the best cybersecurity solutions.The findings emphasize and suggest cybersecurity-related algorithms as a knowledge foundation and resource for upcoming field research that falls within the purview of this study. From the perspective of deep learning, this research serves as a resource and a manual for academics and professionals in the cyber security industry

Keywords

deep learning internet of things artificial intelligence

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Serri Ismael Hamad. (2023). A review of deep learning applied to cyber security. Texas Journal of Engineering and Technology, 22, 49–57. Retrieved from https://zienjournals.com/index.php/tjet/article/view/4272

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