USING MACHINE LEARNING METHODS IN CYBERSECURITY

Mubarakova S.R. Amanzholova S.T. Uskenbayeva R.K.
2022 L.N. Gumilyov Eurasian National University

Eurasian Journal of Mathematical and Computer Applications
2022 #10 Issue 1 69 - 78 pp.

Cybersecurity is an ever-changing field, with advances in technology that open up new opportunities for cyberattacks. In addition, even though serious security breaches are often reported, small organizations still have to worry about security breaches as they can often be the target of viruses and phishing. This is why it is so important to ensure the privacy of your user profile in cyberspace. The past few years have seen a rise in machine learning algorithms that address major cybersecurity issues such as intrusion detection systems (IDS), detection of new modifications of known malware, malware, and spam detection, and malware analysis. In this article, algorithms have been analyzed using data mining collected from various libraries, and analytics with additional emerging data-driven models to provide more effective security solutions. In addition, an analysis was carried out of companies that are engaged in cyber attacks using machine learning. According to the research results, it was revealed that the concept of cybersecurity data science allows you to make the computing process more efficient and intelligent compared to traditional processes in the field of cybersecurity. As a result, according to the results of the study, it was revealed that machine learning, namely unsupervised learning, is an effective method of dealing with risks in cybersecurity and cyberattacks.

Cyberattack , Cybersecurity , Data science , Intrusion detection systems (ids) , Machine learning , Network security

Text of the article Перейти на текст статьи

International Information Technology University, Manas str. 8, Almaty, Kazakhstan

International Information Technology University

10 лет помогаем публиковать статьи Международный издатель

Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026