Generation of Synthetic Data for Honeypot Systems Using Deep Learning Methods

This paper presents studies intended to analyze the methods for generating synthetic data to fill honeypot systems. To choose the generated data types, the topical target objects in the context of honeypot systems are revealed. The existing methods of generation are investigated. Methods for analyzing the quality of generated data in the context of honeypot systems are also analyzed. As a result, the layout of an automated system for generating synthetic data for honeypot systems is developed and the efficiency of its operation is estimated.