Can Machine Learning Predict Cybersecurity Breaches in Enterprise Information Systems? A Supervised Learning Analysis.
DOI:
https://doi.org/10.70577/ASCE/333.352/2025Keywords:
Cybersecurity; Machine Learning; Predictive Analytics; Human Factor; Random Forest.Abstract
This article investigates the ability of supervised machine learning to predict cybersecurity breaches in enterprise environments. Through a comparative analysis of two models, the Support Vector Machine (SVM) and the Random Forest, the study evaluates their effectiveness on a simulated dataset that integrates technical, contextual, and human behavioral variables. Both models achieve a remarkable overall accuracy of 88%, albeit with different strengths: the SVM stands out for its high sensitivity in detecting real breaches, while the Random Forest demonstrates superior consistency and reliability. The most significant finding comes from the variable importance analysis of the Random Forest, which reveals that the human factor—represented by the phishing click-through rate—is the most influential predictor, accounting for 42% of the predictive power. This considerably outperforms technical factors such as severity or number of vulnerabilities. The study concludes that, while machine learning is a powerful toolfor prediction, its greatest value lies in identifying true risk hotspots. Therefore, it is recommended that cybersecurity strategies be reoriented to prioritize human risk mitigation, recognizing it not only as a vulnerability, but as the main predictiveindicator of an incident.
Downloads
References
T. S., -, Theyjakshaya. D., & -, V. Vardhini. A. (2024). Cyber hacking breaches prediction and detection using machine learning. International Journal For Multidisciplinary Research, 6(3), 22653. https://doi.org/10.36948/ijfmr.2024.v06i03.22653 DOI: https://doi.org/10.36948/ijfmr.2024.v06i03.22653
Department of Professional Security Studies, New Jersey City University, , Dhaka, Bangladesh. (2024). Comparative analysis of machine learning algorithms for predicting cybersecurity attack success: A performance evaluation. The American Journal of Engineering and Technology, 6(9), 81–91. https://doi.org/10.37547/tajet/Volume06Issue09-10 DOI: https://doi.org/10.37547/tajet/Volume06Issue09-10
Jada, I., & Mayayise, T. O. (2024). The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review. Data and Information Management, 8(2), 100063. https://doi.org/10.1016/j.dim.2023.100063 DOI: https://doi.org/10.1016/j.dim.2023.100063
Leveraging analytics to predict and prevent security breaches. (2025). Pavion. Retrieved July 11, 2025, from https://pavion.com/resource/leveraging-analytics-to-predict-and-prevent-security-breaches/
Machhindra, P. A., Vijay, B. N., Mahendra, B. S., Rahul, C. A., Anil, P. A., & Sunil, P. R. (2023). Enhancing cyber security through machine learning: A comprehensive analysis. 2023 4th International Conference on Computation, Automation and Knowledge Management (ICCAKM), 1–6. https://doi.org/10.1109/ICCAKM58659.2023.10449547 DOI: https://doi.org/10.1109/ICCAKM58659.2023.10449547
May, R. (2024, September 30). Machine learning algorithms in cybersecurity. Ramsac Ltd. https://www.ramsac.com/blog/machine-learning-algorithms-in-cybersecurity/
Niravkumar Dhameliya. (2024). Machine learning in cybersecurity: A comprehensive analysis of intrusion detection systems. Journal of Sustainable Solutions, 1(4), 38–42. https://doi.org/10.36676/j.sust.sol.v1.i4.22 DOI: https://doi.org/10.36676/j.sust.sol.v1.i4.22
Pujitha, K., Nandini, G., Sree, K. V. T., Nandini, B., & Radhika, D. (2023). Cyber hacking breaches prediction and detection using machine learning. 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), 1–6. https://doi.org/10.1109/ViTECoN58111.2023.10157462 DOI: https://doi.org/10.1109/ViTECoN58111.2023.10157462
Raju, S. (2024). Adaptive security through machine learning with predictive approach to modern cyber threats. International Journal of Computer Applications, 186(50), 6–12. https://doi.org/10.5120/ijca2024924185 DOI: https://doi.org/10.5120/ijca2024924185
Singh, K., & Jha, S. (2021). Cyber threat analysis and prediction using machine learning. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 1981–1985. https://doi.org/10.1109/ICAC3N53548.2021.9725445 DOI: https://doi.org/10.1109/ICAC3N53548.2021.9725445
Srivastava, T. (2019, August 6). 12 important model evaluation metrics for machine learning everyone should know(Updated 2025). Analytics Vidhya. https://www.analyticsvidhya.com/blog/2019/08/11-important-model-evaluation-error-metrics/
Susheela, S., Chandra, N. S., & Priyan, S. S. (2024). Predictive analytics-enabled cyber attack detection. International Journal of Innovative Science and Research Technology (IJISRT), 1242–1247. https://doi.org/10.38124/ijisrt/IJISRT24APR705 DOI: https://doi.org/10.38124/ijisrt/IJISRT24APR705
Tulsyan, R., Shukla, P., Singh, T., & Bhardwaj, A. (2024). Cyber security threat detection using machine learning. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 08(10), 1–6. https://doi.org/10.55041/IJSREM37949 DOI: https://doi.org/10.55041/IJSREM37949
Wang, D. M. (2023, September 1). Machine learning in cybersecurity. Perspectives. https://www.paloaltonetworks.com/perspectives/the-future-of-machine-learning-in-cybersecurity/
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sonia Patricia Córdovez Machado, John Javier Cruz Garzón, Cristian Luis Inca Balseca
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
