ENHANCING CYBERSECURITY THROUGH MALWARE DETECTION BASED ON MACHINE LEARNING TECHNIQUE
Journal: Kufa Journal of Engineering
Year: 2025

The world is now more connected through technology and this has given rise to cyber threats, and malware is one of the threats that a system and data need to guard against. In this paper, we propose a detailed framework wherein state of the art ML methodologies can be employed for malware categorization and identification. In the evaluation of the performances of various ML algorithms, we analyze Random Forest, CatBoost, XGBoost, K-Nearest Neighbors (KNN), Histogram-based Gradient Boosting (Hist