Efficient learning in machine language instruction sequence classification by transfer learning

Abstract

In this paper, we propose a learning method for a machine language instruction sequence which is difficult to prepare a large-scale data set, e.g., packer classification. We used the learning result of the compiler classification which is easy to prepare a large-scale data set. Our examination result shows that it is possible to classify with accuracy of 92% even if only 10 samples of each class are gathered in 30 class classification.