INTELLIGENT NETWORK TRAFFIC MONITORING SYSTEMS IN INDUSTRIAL COMPLEXES
Keywords:
Industrial Internet of Things (IoT), network traffic monitoring, anomaly detection, intrusion detection system (IDS), distributional reinforcement learning (DRL), generative adversarial network (GAN), machine learning, deep learning, edge computing, federated learning, explainable artificial intelligence (XAI), cybersecurity, real-time data analysis, industrial control systems (SCADA, ICS), information security.Abstract
This paper addresses the issues of enhancing management efficiency and information security in industrial networks by improving algorithms for monitoring network traffic within production complexes. The study analyzes the characteristics of data generation and transmission in industrial control systems as well as typical threats affecting the stability of technological processes. It proposes algorithms based on statistical methods and machine learning technologies for analyzing and classifying network flows, enabling real-time detection of anomalous behavior and unauthorized activities. The paper compares traditional and intelligent monitoring approaches, highlighting their advantages and limitations in working with industrial communication protocols. The research results can be applied in developing platforms for Industrial Internet of Things (IIoT), automated process control systems (APCS/SCADA), and industrial network cybersecurity solutions.
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