Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection

Understand

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.