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This paper presents a study of cyberattack classification using machine learning methods on the KNIME platform. The topic is relevant due to the growth of vulnerabilities in digital systems. Various machine learning models, including Decision Tree Learner, Random Forest Learner, Naive Bayes Learner, Tree Ensemble Learner, and Gradient Boosted Trees Learner, are examined to identify the most effective approach for attack classification. The paper includes an overview of key attack characteristics and offers practical recommendations for improving protection.-->

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Machine Learning Methods for Cyber Attacks Detection and Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Vitalii Lapin,
  • Walaa H. Elashmawi,
  • Veronika Abakumova,
  • Marina Tokmakova

摘要

This paper presents a study of cyberattack classification using machine learning methods on the KNIME platform. The topic is relevant due to the growth of vulnerabilities in digital systems. Various machine learning models, including Decision Tree Learner, Random Forest Learner, Naive Bayes Learner, Tree Ensemble Learner, and Gradient Boosted Trees Learner, are examined to identify the most effective approach for attack classification. The paper includes an overview of key attack characteristics and offers practical recommendations for improving protection.-->