Information technology for the classification of data monitoring systems and data transmission networks for cyber threat detection

Nowadays, the security of critical infrastructure (CI) is a key challenge in the modern digital environment. The growing complexity of information systems and cyberattack methods necessitates the development of effective approaches for threat detection and classification. This paper presents a methodology for classifying types of threats for critical infrastructure based on network data. It proposes the use of machine learning and anomaly detection methods to identify malicious activities. Modern threat classification algorithms and neural networks are analyzed. The obtained results demonstrate the effectiveness of the proposed approach in detecting and identifying potential threats, thereby enhancing the cybersecurity level of critical infrastructure.