Evaluation and Implementation of Malware Classification Using Random Forest Machine Learning Algorithm
Saifaldeen Alabadee, Karam Thanon · 2021
Malware classification is one of the most important issues in Information security, because of the huge new numbers of these malwares. Therefore, more classification methods have been proposed. Random forest (RF) is one of the extremely method in many studies or deferent feature extraction methods. It has been considered as one of the efficient methods of malware classification due to it is accurate results. In this paper, machine learning based RF classifier had been proposed to evaluate the performance of the Random Forest implementation. The RF classifier showed high performance as a detector. It has a good capability of classifying huge number of features with unimportant features. Both training and classifying accuracy have increased by reduction of the number of training feature in dataset. The RF classifier have achieved 95.3% of accuracy.