CYBERSECURITY
Keywords:
machine learning, cybersecurity, anomaly, classification, network, protection, model, algorithm, threat, risk, monitoring.Abstract
This scientific article provides an in-depth analysis of the application of machine learning (ML) technologies in the field of cybersecurity, their effectiveness, and the challenges associated with their practical implementation. In the process of digital transformation, the rapid expansion of networks, services, and information systems has significantly complicated the task of ensuring cybersecurity. In such conditions, traditional rule-based security tools are unable to fully address the dynamic, adaptive, and covert nature of modern cyberattacks. Therefore, ML-based approaches are recognized as one of the most promising methods for detecting anomalies in network traffic, classifying malicious software, identifying phishing messages, analyzing behavioral patterns, and predicting cyberattacks at early stages.
The article examines the advantages and limitations of supervised, unsupervised, and deep learning models, as well as their robustness under real-world threat conditions. It also highlights risks related to mislabeled data, data poisoning, adversarial attacks, and computational resource constraints. The study proposes scientifically grounded approaches for integrating ML algorithms into various stages of cybersecurity processes — detection, classification, monitoring, and response. The findings contribute to the development of effective strategies for applying ML technologies and support the advancement of intelligent protection systems within the cybersecurity ecosystem.
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