Comparing performance of malware classification on automated stacking

Abstract

Stacking in machine learning allows multiple classification or regression algorithms to work together with a goal to enhance performance. To understand the risky properties of malware contamination in a system, it is important to accurately classify malware type first. Malware classification is the procedure of labeling the families of malware. In this paper, we automate stacking with 7 machine learning algorithms and 3 boosting algorithms. The experimental results show a 99.2% accuracy is achieved from a multilayer perceptron network with AdaBoost classifier, which outperforms other models on the malware API call dataset.