GENERATION OF SYNTHETIC DATA FOR DETECTION OF DDOS ATTACKS USING MACHINE LEARNING

Novikov Evgeny Ivanovich, Afanasyev Vadim Vladimirovich, Kunin Nikita Timofeevich

The constant increase in the number of distributed denial of service (DDoS) attacks necessitates the improvement of detection approaches. This paper examines the possibility of using machine learning methods to improve the accuracy of DDoS attack detection. It is noted that the most important step in developing DDoS attack detection models is the creation of a training dataset. A technology for generating training data for models using virtual machines and specialized tools for generating and analyzing network traffic is proposed.

Keywords:cybersecurity, intrusion detection system, DDoS attacks, machine learning, synthetic data, network traffic.