One of the new developments in static malware detection has been the use of deep learning for end-to-end machine learning for malware detection. In this setting, we completely skip all feature engineering; we need not have any knowledge of the PE header or other features that may be indicative of PE malware. We simply feed a stream of raw bytes into our neural network and train. This idea was first suggested in https://arxiv.org/pdf/1710.09435.pdf. This architecture has come to be known as MalConv, as shown in the following screenshot:
