  This study conducts a thorough examination of malware detection using machine
learning techniques, focusing on the evaluation of various classification
models using the Mal-API-2019 dataset. The aim is to advance cybersecurity
capabilities by identifying and mitigating threats more effectively. Both
ensemble and non-ensemble machine learning methods, such as Random Forest,
XGBoost, K Nearest Neighbor (KNN), and Neural Networks, are explored. Special
emphasis is placed on the importance of data pre-processing techniques,
particularly TF-IDF representation and Principal Component Analysis, in
improving model performance. Results indicate that ensemble methods,
particularly Random Forest and XGBoost, exhibit superior accuracy, precision,
and recall compared to others, highlighting their effectiveness in malware
detection. The paper also discusses limitations and potential future
directions, emphasizing the need for continuous adaptation to address the
evolving nature of malware. This research contributes to ongoing discussions in
cybersecurity and provides practical insights for developing more robust
malware detection systems in the digital era.
