Multi-relational Instruction Association Graph for Binary Comparison. (arXiv:2206.12236v1 [cs.CR])

Cross-architecture binary similarity comparison is essential in many security
applications. Recently, researchers have proposed learning-based approaches to
improve comparison performance. They adopted a paradigm of instruction
pre-training, individual binary encoding, and distance-based similarity
comparison. However, instruction embeddings pre-trained on external code corpus
are not universal in diverse real-world applications. And separately encoding
cross-architecture binaries will accumulate the semantic gap of instruction
sets, limiting the comparison accuracy. This paper proposes a novel
cross-architecture binary similarity comparison approach with multi-relational
instruction association …

binary