Malware Classification with CoAtNet: Marrying Convolution and Attention for Visual Images pytorch.
The increasing threat of malicious software has pushed malware detection into the realm of convolutional neural networks and deep learning. In this paper we will look at some of the machine learning methods already applied to malware detection and utilise a recently proposed method for visual classification in the field of malware detection called CoAtNet. We will also look at malware detection with convolutional neural networks with and without data augmentation.
The dataset is obtained from https://www.kaggle.com/keerthicheepurupalli/malimg-dataset9010