Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset

Abstract

In recent times, malware visualization has become very popular for malware
classification in cybersecurity. Existing malware features can easily identify
known malware that have been already detected, but they cannot identify new
and infrequent malwares accurately. Moreover, deep learning algorithms
show their power in term of malware classification topic. However, we found
the use of imbalanced data; the Malimg database which contains 25 malware
families don’t have same or near number of images per class. To address these
issues, this paper proposes an effective malware classifier, based on costsensitive deep learning. When performing classification on imbalanced data, some classes get less accuracy than others. Cost-sensitive is meant to solve this issue, however in our case of 25 classes, classical cost-sensitive weights wasn’t effective is giving equal attention to all classes. The proposed approach improves the performance of malware classification, and we demonstrate this improvement using two Convolutional Neural Network models using functional and subclassing programming techniques, based on loss, accuracy, recall and precision.