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

arXiv:2403.02232v1 Announce Type: cross
Abstract: 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 …

abstract advance aim api arxiv capabilities classification cs.ai cs.cr cs.lg cybersecurity dataset detection ensemble evaluation machine machine learning machine learning techniques malware malware detection mitigating threats study threats type