An Assessment of the Effectiveness of Pretrained Neural Networks for Malware Detection

Abstract

The speed at which malware develops is much faster than analysts are able to analyze it, and it is now a dangerous threat to businesses, critical infrastructure and individuals, forcing anti-virus companies to develop fast and reliable methods for detecting malware. Transfer learning, a deep learning technique, has special abilities such as training speed, lower training dataset size requirements and reduced demand for domain expert knowledge. In this paper, we compared the prediction and model generation performances by using modern convolutional neural networks (Resnet, DenseNet, VGG and AlexNet), which have proven to be successful in the visual classification problem for malware detection. In addition, a dataset containing 14,972 malware samples and 14,500 samples of benign files with some function similar to that possessed by malwares was proposed and used in the study. The best accuracy obtained was 96.35% corresponding to the DenseNet-201 architecture.