Application of Synthetic Data to the Problem of Anomaly Detection in the Field of Information Security

Abstract

Synthetic data are highly relevant for machine learning. Modern algorithms to generate synthetic data make it possible to generate data that are very similar in their statistical properties to the original data. Synthetic data is used in practice in a wide range of tasks, including those related to data augmentation. The author of the article proposes a method of data augmentation combining the approaches of increasing the sample size using synthetic data and synthetic anomaly generation. This method has been used to address the information security problem of anomaly detection in server logs to detect attacks. The model trained for the task presents high results. This demonstrates the effectiveness of the use of synthetic data to increase sample size and generate anomalies, as well as the ability to use these approaches together with high efficiency.