A Comparative Study of Machine Learning Models for Cyber-Attack Detection Emphasizing Performance and Legal Responsibility

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Published: May 26, 2026
DOI: https://doi.org/10.63913/jcl.v2i2.26
Keywords:
Cybersecurity, Machine Learning, Network Traffic
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👤 Nevita Cahaya Ramadani
🏢 Informatics Study Program, Faculty of Science and Technology, Universitas Nahdlatul Ulama Purwokerto, Indonesia
👤 Sukron Fahreza
🏢 Informatics Study Program, Faculty of Science and Technology, Universitas Nahdlatul Ulama Purwokerto, Indonesia

The rapid growth of cyber threats and the increasing complexity of digital infrastructures have created an urgent need for intelligent and automated systems capable of identifying malicious activities within network environments. This study investigates the application of five machine learning algorithms, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost, for the detection of cyber-attacks using a network traffic dataset.

Ramadani, N. C., & Fahreza, S. (2026). A Comparative Study of Machine Learning Models for Cyber-Attack Detection Emphasizing Performance and Legal Responsibility. Journal of Cyber Law, 2(2), 78–92. https://doi.org/10.63913/jcl.v2i2.26

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Issue
Vol. 2 No. 2 (2026): Regular Issue: June 2026
Section
Articles