Comprehensive Evaluation of Machine Learning and Deep Learning Approaches for Malware Detection
DOI:
https://doi.org/10.61722/jssr.v3i6.6480Keywords:
Cybersecurity, Malware Detection, Machine Learning, GRU, Random Forest, Gradient Boosting, LSTM, RNNAbstract
In an era of advancing technology, breakthrough innovations, state- of-the-art hardware development, increased computing capacity through cloud computing and supercomputers, and collaboration between research institutions and industries are the primary driv- ing forces. However, this is also accompanied by the rise in malware attacks due to increasingly complex systems and the adoption of new technologies providing vulnerabilities for attackers. High pro- cessing capabilities are exploited to develop undetectable malware, while collaboration among cybercriminals is on the rise. There- fore, effective cybersecurity protection and efforts are becoming increasingly crucial. In this study, we compared GRU, Random For- est, Gradient Boosting, LSTM, and RNN models for malware attack detection, with experimental results showing that the Gradient Boosting model achieved the highest accuracy of 99.98%.
References
Adeyeye, O., Akanbi, I., Emeteveke, I., & Emehin, O. (2024). Leveraging Secured Ai-Driven Data Analytics For Cybersecurity: Safeguarding Information And Enhancing Threat Detection. International Journal of Research Publication and Reviews. https://doi.org/10.55248/gengpi.5.1024.2911.
Akhtar, M., & Feng, T. (2023). Evaluation of Machine Learning Algorithms for Malware Detection. Sensors (Basel, Switzerland), 23. https://doi.org/10.3390/s23020946.
Alomari, E., Nuiaa, R., Alyasseri, Z., Mohammed, H., Sani, N., Esa, M., & Musawi, B. (2023). Malware Detection Using Deep Learning and Correlation-Based Feature Selection. Symmetry, 15, 123. https://doi.org/10.3390/sym15010123.
Aslan, Ö., & Samet, R. (2020). A Comprehensive Review on Malware Detection Approaches. IEEE Access, 8, 6249-6271. https://doi.org/10.1109/ACCESS.2019.2963724.
Azeem, M., Khan, D., Iftikhar, S., Bawazeer, S., & Alzahrani, M. (2023). Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches. Heliyon, 10. https://doi.org/10.1016/j.heliyon.2023.e23574.
Callens, A., Morichon, D., Abadie, S., Delpey, M., & Liquet, B. (2020). Using Random forest and Gradient boosting trees to improve wave forecast at a specific location. Applied Ocean Research, 104, 102339. https://doi.org/10.1016/j.apor.2020.102339.
Cao, B., Li, C., Song, Y., Qin, Y., & Chen, C. (2022). Network Intrusion Detection Model Based on CNN and GRU. Applied Sciences. https://doi.org/10.3390/app12094184.
Falowo, O., Ozer, M., Li, C., & Abdo, J. (2024). Evolving Malware and DDoS Attacks: Decadal Longitudinal Study. IEEE Access, 12, 39221-39237. https://doi.org/10.1109/ACCESS.2024.3376682.
Shaukat, K., Luo, S., & Varadharajan, V. (2023). A novel deep learning-based approach for malware detection. Eng. Appl. Artif. Intell., 122, 106030. https://doi.org/10.1016/j.engappai.2023.106030.
Varma, S. (2024). AI-Enhanced Cloud Security: Proactive Threat Detection and Response Mechanisms. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i06.31587.
Vinayakumar, R., Alazab, M., Member, I., Poornachandran, P., & Venkatraman, A. (2019). Robust Intelligent Malware Detection Using Deep Learning. IEEE Access, 7, 46717-46738. https://doi.org/10.1109/ACCESS.2019.2906934.
Xu, C., Shen, J., Du, X., & Zhang, F. (2018). An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units. IEEE Access, 6, 48697-48707. https://doi.org/10.1109/ACCESS.2018.2867564.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 JOURNAL SAINS STUDENT RESEARCH
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
