Malware Detection Using a Machine-Learning Based Approach

Abstract

The purpose of this research work is to study the usage of machine learning in detecting malware. This paper presents a versatile framework, in which a dataset of more than 130000 files has been analyzed, to train and test four machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting; The performance of each algorithm in malware classification, has been studied based on the: Accuracy, execution time, rate of false positives and false negatives, and area under the Receiver Operating Characteristic curve.