MACHINE LEARNING METHODS IN PREDICTING AND PREVENTING CYBER ATTACKS
Alexander V. Loschilin
Master’s Student, Department of Information Security,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Vladislav G. Yarikov
Candidate of Sciences (Pedagogy), Associate Professor,
Department of Information Security,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Arina V. Nikishova
Candidate of Sciences (Engineering), Associate Professor,
Department of Information Security,
Volgograd State University
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Prosp. Universitetsky, 100, 400062 Volgograd, Russian Federation
Abstract. This article examines the role of machine learning (ML) in predicting and preventing cyberattacks, detailing the use of supervised, unsupervised, and reinforcement learning techniques. The article discusses the benefits and challenges of integrating ML into cybersecurity, including accuracy, privacy concerns, and technical difficulties. Solutions to overcome these challenges, such as continuous model refinement and ethical compliance, are also proposed, highlighting the potential of ML to enhance cyber defense strategies. Key aspects of the use of machine learning in cybersecurity are reviewed: machine learning techniques, their application to predicting and preventing cyberattacks, and related issues and challenges. It is clear that machine learning provides powerful tools to improve defense against cyber threats by automatically detecting and responding to attacks. However, there are challenges to implementing these technologies, including the need to ensure model accuracy, adapt to new threats, protect data and privacy, and overcome technical and operational limitations. An integrated approach to training models, balancing the use of different machine learning techniques, developing ethical principles and standards, and investing in infrastructure and skills will help overcome these challenges. Thus, while machine learning represents a promising trend in cybersecurity, its successful application requires careful consideration of both technical capabilities and potential risks.
Key words: machine learning, cybersecurity, predicting cyberattacks, preventing cyberattacks, machine learning challenges, data privacy.
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