Abstract: An automated analysis method for large-scale embedded firmware was designed to get device information, such as file system type, operating system type, or CPU instruction set. But it was difficult to know whether it was decoded successfully during automated firmware analysis. To solve this problem, a firmware decoding status detection method was proposed based on classification and regression tree algorithm. The dataset contained 6 160 firmware samples and 1 823 disassembled binary files that were collected from firmware decoding. The experiments conducted on the dataset demonstrated that the proposed method had a considerable performance comparing with other classifiers, whose precision and recall rate are both above 96%.

Key words: embedded device firmware, classification and regression tree, status detection