Detecting Malware Using Deep Neural Networks

This article proposes a method for detecting malicious executable files by analyzing disassembled code. This method is based on a static analysis of assembler instructions of executable files using a special neural network model, whose architecture is also presented in this article. In addition, the effectiveness of the method is demonstrated using several different metrics, showing a significant reduction in Type-II errors compared to other state-of-the-art methods. The obtained results can be used as a basis for designing systems for thestatic analysis of malware.