Rifki Sariful Munzi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Rifki Sariful Munzi has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Aqshal Bilnandzari Mustafa
Faculty of Engineering Computer and Design, Nusa Putra University, Sukabumi 43152, Indonesia
1 shared publication - Ivana Lucia Kharisma
Universitas Nusa Putra
1 shared publication - Kamdan
Faculty of Engineering, Computer and Design, Nusa Putra University, Sukabumi 43152, Indonesia
1 shared publication - Yoga Pratama
Faculty of Engineering Computer and Design, Nusa Putra University, Sukabumi 43152, Indonesia
1 shared publication
Latest Publications
- Open Access
- 9 September 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 107 (1) , 76
Static Malware Detection and Classification Using Machine Learning: A Random Forest Approach
Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset was collected from MalwareBazaar, and static features such as PE headers, entropy, and API calls were extracted. Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities. Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use. These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems.
- 9 September 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 107 (1) , 76
Open AccessStatic Malware Detection and Classification Using Machine Learning: A Random Forest Approach
Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset was collected from MalwareBazaar, and static features such as PE headers, entropy, and API calls were extracted. Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities. Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use. These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems.