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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Ikram Ben Abdel Ouahab
University Abdelmalek Essaadi
Computer Science & IT Engineering

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Improve malware classifiers performance using cost-sensitive learning for imbalanced dataset Ikram Ben Abdel Ouahab; Lotfi Elaachak; Mohammed Bouhorma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1836-1844

Abstract

In recent times, malware visualization has become very popular for malwareclassification in cybersecurity. Existing malware features can easily identifyknown malware that have been already detected, but they cannot identify newand infrequent malwares accurately. Moreover, deep learning algorithmsshow their power in term of malware classification topic. However, we foundthe use of imbalanced data; the Malimg database which contains 25 malwarefamilies don’t have same or near number of images per class. To address theseissues, 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.
Co-Authors Lotfi Elaachak Mohammed Bouhorma