Application of Deep Learning Methods in Cybersecurity Tasks. Part 2

Abstract

The purpose of the article: comparative analysis of methods for solving various cybersecurity problems based on the use of deep learning algorithms. Research method: Systematic analysis of modern methods of deep learning in various cybersecurity applications, including intrusion and malware detection, network traffic analysis, and some other tasks. The result obtained: classification scheme of the considered approaches to deep learning in cybersecurity, and their comparative characteristics by the used models, characteristics, and data sets. The analysis showed that many deeper architectures with a large number of neurons on each layer show better results. Recommendations are given for using deep learning methods in cybersecurity applications. The main contribution of the authors to the research of deep learning methods for cybersecurity tasks is the classification of the subject area; conducting a general and comparative analysis of existing approaches that reflect the current state of scientific research.