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All Journal International Journal of Advances in Intelligent Informatics
Muhammad ‘Arif Bin Mohamad
Universiti Malaysia Pahang Al-Sultan Abdullah
Computer Science & IT

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Machine learning model for classifying the severity level of cyber security attacks Imam Riadi; Sri Winiarti; Herman Yuliansyah; Muhammad ‘Arif Bin Mohamad
International Journal of Advances in Intelligent Informatics Vol 12, No 2 (2026): May 2026
Publisher : Universitas Ahmad Dahlan

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Abstract

Cyberattacks are becoming increasingly sophisticated, necessitating defense mechanisms that go beyond simple detection to include severity assessment for prioritizing mitigation. This study proposes a comprehensive machine learning framework to classify cyberattack severity levels (Low, Medium, High) using a modern, high-dimensional dataset. Addressing the critical challenge of class imbalance, the research integrates the Synthetic Minority Oversampling Technique (SMOTE) with a rigorous feature selection process involving SelectKBest. Four algorithms Naive Bayes, K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) were evaluated using 10-fold cross-validation. The results demonstrate that the SVM model with an RBF kernel achieves superior performance with an accuracy of 97.30% and a False Negative Rate (FNR) of only 3.1% for high-severity threats. This research contributes a robust, data-driven approach to severity classification that effectively handles feature non-linearity and class imbalance, offering actionable insights for real-time security operations.
Co-Authors Herman Yuliansyah Imam Riadi Sri Winiarti