Visual Based Malware Clustering Using Convolution Neural Network

Abstract

As the popularity of Internet of Things (IoT) devices expands in industries and residences, their low processing power and inadequate security make them ideal targets for attackers. Traditional signature-based methods for detecting malware are inefficient against new malware since a small modification in the malware's source code can modify its signature, making it impossible to detect. Understanding the basics of malware behaviour and combatting hackers requires the classification of malware samples. In this study, we examine an image-based classification of malware in which nine malware families were categorised using a convolution neural network (CNN). Using kfold stratified cross-validation, our model attained a promising 89.5% accuracy in training and 82% accuracy in validation.