ANALYSIS OF THE EFFECTIVENESS OF BORDER TRAFFIC ANOMALY DETECTION BASED ON MACHINE LEARNING MODELS

The article presents an approach to constructing a real-time anomaly detection model for DoS (Denial of Service) network traffic and its integration into a monitoring system. This opens new opportunities for visualization, investigation, and development of intrusion detection systems (IDS) and their digital twins, providing a flexible platform for modeling cyber-physical threats and responding to them. The study synthesizes a range of models with various neural network architectures, including CNN (Convolutional Neural Networks), LSTM (Long Short-Term Memory), and Autoencoder variants, performs a comparative analysis, and selects an effective model for predicting anomalies in network traffic using diverse metrics. The chosen model exchanges data with the Node-RED environment, which implements the traffic monitoring system and provides graphical representation of intrusion detection results, automated responses, and additional network traffic simulation. The model functions as a digital twin of the anomaly detection system. This approach enables the development of a prototype system that can be rapidly deployed without the need for complex computational resources or cluster systems.