Improving Malware Detection Classification Accuracy with Feature Selection Methods and Ensemble-based Machine Learning Methods

Abstract

Malware is evolving serious threats to internet security. The classification of malware is extremely crucial in recent days. The traditional models are failed to achieve to get effective accuracy rate and the machine learning models are the basic models that accomplish the task of classification in a certain way, but in recent decades malware attacks are very drastic and difficult to achieve zero-day attacks. To compete with new malware, ensemble methods are highly effective and give better results of accuracy. In this paper, we propose a framework that combines the exploit of both feature selection methods and ensemble learning classifiers and gives better results of classification. In the experimental results, we prove that this combination of methods gives better classification with high accuracy of 100% with the Random Forest ensemble classifier.