Extracting Scheme of Compiler Information using Convolutional Neural Networks in Stripped Binaries

The strip binary is a binary from which debug symbol information has been deleted, and therefore it is difficult to analyze the binary through techniques such as reverse engineering. Traditional binary analysis tools rely on debug symbolic information to analyze binaries, making it difficult to detect or analyze malicious code with features of these strip binaries. In order to solve this problem, the need for a technology capable of effectively extracting the information of the strip binary has emerged. In paper, focusing the fact that the byte code of the binary file is generated very differently depending on compiler version, optimazer level, etc. For effective compiler version extraction, the entire byte code is read and imaged as the target of the stripped binaries and this is applied to the convolution neural network. Finally, we achieve an accuracy of 93.5%, and we provide an opportunity to analyze stripped binary more effectively than before.