Использование методов машинного обучения для повышения эффективности систем противодействия многоэтапных кибератак

Инженерный вестник Дона. 2026. № 2(134). Статья 7.

Lebedev O. B., Левченко Д. Д., Черкасов Р. И.

This article analyzes the impact of artificial intelligence (AI) and machine learning technologies on the development and transformation of cyberthreats and the creation of highly effective cyberdefense systems. Key trends in AI evolution are discussed, including data-, model-, application-, and human-centric approaches, and their role in shaping both defensive and offensive capabilities. It is shown that attackers actively use AI to automate reconnaissance, personalize attacks, evade detection systems, and conduct complex multi-stage cyberattacks. The main types of impact on machine learning systems are analyzed: data manipulation, adversarial examples, attacks on models and their infrastructure. Modern defense methods that improve model robustness, data security, and the resilience of AI systems are presented. The idea of the need to integrate intelligent approaches at all levels of the cyberdefense architecture and develop trusted, interpretable, and resilient machine learning models to counter new classes of threats is put forward.

Keywords: машинное обучение, информация, information, protection, защищенность, cybersecurity, кибербезопасность, cyberattack, кибератака, artificial intelligence, machine learning, искусственный интеллект