Taxonomy on RapidMiner Using Machine Learning

Abstract

Malware Classification is an important factor in security of Computer and Network Systems. Malware Classification means to classify the malware samples into their respective families and subfamilies. In this work we have used malware dataset having multiple signatures. Our objective here is to provide efficient machine learning algorithm which provide best accuracy for classification of malware into their respective families. We use RapidMiner tool and four different machine learning algorithms to analyze dataset. Best accuracy we get is of 64.05% using Decision Tree algorithm and split validation technique for splitting of dataset into training and test set.