Methodology for malware classification using a random forest classifier

Malware analysis using machine learning techniques has been the subject of study in recent years as a new alternative for efficient detection of malicious behaviour patterns in different operating systems. Recent advances in this research field have proposed different algorithms employing information extraction and feature selection tasks, aiming to cover different types of data and improving several performance metrics. In this work is proposed the use of an assembly classifier, better known as Random Forest, that improves the performance of other well-known algorithms by aggregating individual class predictions to combine into a final prediction. A case study is presented using two different datasets of malware, that through data preparation techniques is enhanced the quality of data to strengthen the classifier training.