Performance evaluation of various ensemble classifiers for malware detection

Today there is a tremendous flow of data to several information systems within fraction of seconds. At the same time the vulnerabilities in the digital infrastructure have been a serious threat to the security of information. The presence of malware in sensitive data may incur huge financial loss or even causes life threatening events. This paper discusses the performance of different ensemble classification algorithms in the detection of malware present in the data. Two benchmark malware datasets are used for evaluation. The various ensemble algorithms like Bagging ensemble, Random Forest, Gradient descent boosting, AdaBoost, Stacking Ensemble, XGBoost, Light GBM Ensemble are compared based on several evaluation metrics namely accuracy, precision (positive, negative), recall (sensitivity and specificity), F1-score, Jaccard score and Hamming Loss. The XGBoost ensemble has resulted in 99% accuracy during the identification of malware with a negligible Hamming loss of 0.014 and 0.013 on the two different data sets.Copyright (c) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Innovative Technology for Sustainable Development.