High-Accuracy Malware Classification with a Malware-Optimized Deep Learning Model

Malware threats are a serious problem for computer security, and the ability to detect and classify malware is critical for maintaining the security level of a computer. Recently, a number of researchers are investigating techniques for classifying malware families using malware visualization, which convert the binary structure of malware into grayscale images. Although there have been many reports that applied CNN to malware visualization image classification, it has not been revealed how to pick out a model that fits a given malware dataset and achieves higher classification accuracy. We propose a strategy to select a Deep learning model that fits the malware visualization images. Our strategy uses the fine-tuning method for the pre-trained CNN model and a dataset that solves the imbalance problem. We chose the VGG19 model based on the proposed strategy to classify the Malimg dataset. Experimental results show that the classification accuracy is 99.72 %, which is higher than other previously proposed malware classification methods.