MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms two datasets obtained static dynamic analysis of real malicious benign samples. We then compare their success rates—both mutually with other algorithms, such as random forest, decision tree, support vector machine, naïve Bayes which we compared in our previous work same datasets. The best performing classification models, algorithm, achieved 91.9% detection accuracy 98.2% sensitivity, 0.853 AUC, 0.949 F1 score dataset, 96.4% 98.5% 0.940 0.977 dataset. Then, exported used them proposed MLMD program, automating process allowing trained to be for new