Visual malicious software detection device and method based on deep neural network

The invention discloses a visual malicious software detection device and method based on a deep neural network, and the method comprises the steps: converting an executable file sample into a bytes file and an asm file through employing a disassembly technology, combining a normal software data set collected and marked by a user with a famous BIG 2015 malicious software data set, and obtaining a balance experiment data set; in order to effectively extract high-dimensional features in a data sample, converting the sample further into an RGB three-channel image by using a visualization technology combined with data enhancement. The invention also provides unique deep neural network classification architecture, which is used for improving the performance of the detection method. The method disclosed by the invention is explained from other numerous neural network model methods; the superiority of the RGB three-channel image in the aspect of malicious software detection compared with a gray level image is verifie