DETECTION OF CYBERATTACKS IN NETWORK TRAFFIC BASED ON MACHINE LEARNING ALGORITHMS

Abstract

The article examines the role and significance of machine learning (ML) methods as the methodological foundation of modern artificial intelligence. It is substantiated that the ability of information systems to self-learn and adapt in dynamic environments is a key factor in their effectiveness. The rapid growth in demand for ML technologies across all spheres of human activity is analyzed, which inevitably leads to the accumulation and processing of vast volumes of sensitive information. The concentration of such data creates new threat vectors, as it becomes a priority target for cyber adversaries. Special attention is paid to the implementation of ML algorithms in modern security ecosystems. The experience of using leading industrial solutions that replace traditional signature-based approaches with intelligent analysis is reviewed. The mechanisms for implementing network attacks aimed at poisoning initial data and manipulating the training process are described in detail. The results of the conducted experimental studies confirm that the use of irrelevant or compromised models in cybersecurity systems creates an illusion of security, leaving critical infrastructure vulnerable to targeted attacks. The article offers a conceptual outlook on the necessity of developing secure training protocols to ensure the resilience of intelligent systems.