Mal-Detect: An intelligent visualization approach for malware detection

Abstract

Recent outbreaks of pandemics have deepened the adoption and use of IT-based systems. This development has led to an exponential increase in cyberattacks caused by malware. Current approaches (static, dynamic and hybrid) for detecting malware still exhibit low efficiency when subjected to sophisticated malware. This work used an ensemble technique consisting of Deep Convolutional Neural Network and Deep Generative Adversarial Neural Network (Mal-Detect) to analyse, detect, and categorise malware. The proposed Mal-Detect first converts both malware and benign file binaries into RGB binary images. New malware images are then generated using a deep generative adversarial neural network from original malware samples. The generated malware images with original malware and benign files images are pre-processed and trained with Deep Convolutional Neural Networks to extract important features from the dataset. The effectiveness of Mal-Detect was evaluated against three benchmark datasets; MaleVis, Mallmg and Virushare. The results of the evaluation showed that Mal-Detect outperforms other state of art techniques with an accuracy of 99.8% and an average accuracy of 96.77% on all malware datasets tested. These results showed that Mal-Detect can be deployed for detecting all categories of malware.