Hybrid SVM–Random Forest Ensemble Superiority for Static Malware Detection: A Comparative Study

. Akhtar, et al. (2023). AI-enabled approach for enhancing obfuscated malware detection [2]: a hybrid ensemble learning with combined
feature selection techniques. Journal of Ambient Intelligence and Humanized Computing.
2. Heliyon (2023). Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.
3. Smmarwar, S. K., et al. (2024). Android malware detection and identification frameworks: A comprehensive review [4]. Journal of
Information Security and Applications.
4. Damodaran, A., et al. (2022). A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection [5]. arXiv preprint.
5. Kim, H., et al. (2023). Performance evaluation of ensemble learners in cybersecurity: A review. Computers & Security.
6. Ablayeva, O. (2025). Comparative Analysis of Random Forest, SVM, and LSTM for Malware Detection. International Journal of AI.
7. Kamdan, et al. (2025). Static Malware Detection and Classification [8] Using Machine Learning. MDPI.
8. Moreno-Lara, I. (2025). Static Malware Detection through Ensemble Feature Selection.Informatica.
9. Rezaei, T., et al. (2021). A PE header-based method [10] for malware detection using deep learning. Expert Systems with Applications.
10. Wang, Y., et al. (2023). Opcode-based malware detection [11] using ensemble methods and feature selection. IEEE Access.
11. Patel, S., et al. (2024). API call sequence analysis [12] with hybrid classifiers for advanced malware detection. International Journal of
Computer Science and Network Security.
12. Alharthi, A., et al. (2025). A comparative study of machine learning and deep learning for malware detection. Journal of Big Data
Analytics in Transport.
13. Borate, V., et al. (2024). A Novel Technique for Malware Detection Analysis Using Hybrid Machine Learning Model [14]. ResearchGate.14. Yousuf, M. I., et al. (2023). Windows malware detection based on static analysis with multiple features [15]. PeerJ Computer Science.
15. Gupta, A., et al. (2023). Comparative study of machine learning algorithms for IoT malware detection [16]. Journal of Information Security
and Cybercrime.
16. ResearchGate (2025). Comparison of Support Vector Machine and Random Forest Method on Static Analysis Windows Portable
Executable (PE) Malware Detection.
17. Zhu, L., et al. (2023). Stacking ensemble for malware detection [18]: A feature-level and model-level combination approach. Expert
Systems with Applications.
18. ResearchGate (2024). Hybrid RFSVM [19]: Hybridization of SVM and Random Forest Models for Detection of Fake News.
19. Anderson, H. S., & Roth, P. (2018). EMBER: An open dataset for training static PE malware machine learning models. USENIX Security
Symposium.
20. Zhang, X., et al. (2025). EMBER 2024: A new benchmark for holistic malware classification.IEEE Transactions on Information Forensics
and Security.
21. Yang, L., et al. (2021). BODMAS: An open dataset for learning based temporal analysis of malware. Deep Learning and Security
Workshop (DLS).
22. Chen, W., et al. (2024). Robustness of Random Forest and SVM against adversarial malware samples. Journal of Network and Computer
Applications.
23. Singh, A., et al. (2025). Hybrid SVM-RF ensemble for ransomware detection using static features. International Journal of Advanced
Computer Science and Applications.
24. Sulaiman, R. B., et al. (2025). Metaheuristic-Driven Feature Selection with SVM and KNN for Robust DDoS Attack Detection: A
Comparative Study. Journal of Cybersecurity and Information Security.
25. Sherazi, S. N. A., et al. (2025). Hybrid Analysis Model for Detecting Fileless Malware.Electronics (MDPI).