Investigation of the problem of classifying unbalanced datasets in identifying distributed denial of service attacks

Abstract

This paper examines the impact of data balancing algorithms in the network traffic classification problem on various types of distributed denial of service attacks on the CICDDoS2019 dataset, which contains information about reflection-based and exploitation-based attacks. The results of computational experiments have shown the effectiveness of data balancing algorithms such as naive random sampling, synthetic minority sampling, and adaptive synthetic sampling in identifying network attacks. A comparative analysis of various data sampling approaches has shown that the adaptive synthetic sampling method with the random forest algorithm demonstrates the highest classification accuracy.