FASER: Binary Code Similarity Search through the use of Intermediate Representations

• URL: http://arxiv.org/abs/2310.03605v3
• Date: Wed, 29 Nov 2023 14:30:29 GMT
• Title: FASER: Binary Code Similarity Search through the use of Intermediate Representations
• Authors: Josh Collyer, Tim Watson and Iain Phillips
• Abstract summary: Cross-Architecture Binary Code Similarity Search has been explored in numerous studies. We propose Function as a String Encoded Representation (FASER) to create a model capable of cross architecture function search.
• Score: 0.8594140167290099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Being able to identify functions of interest in cross-architecture software is useful whether you are analysing for malware, securing the software supply chain or conducting vulnerability research. Cross-Architecture Binary Code Similarity Search has been explored in numerous studies and has used a wide range of different data sources to achieve its goals. The data sources typically used draw on common structures derived from binaries such as function control flow graphs or binary level call graphs, the output of the disassembly process or the outputs of a dynamic analysis approach. One data source which has received less attention is binary intermediate representations. Binary Intermediate representations possess two interesting properties: they are cross architecture by their very nature and encode the semantics of a function explicitly to support downstream usage. Within this paper we propose Function as a String Encoded Representation (FASER) which combines long document transformers with the use of intermediate representations to create a model capable of cross architecture function search without the need for manual feature engineering, pre-training or a dynamic analysis step. We compare our approach against a series of baseline approaches for two tasks; A general function search task and a targeted vulnerability search task. Our approach demonstrates strong performance across both tasks, performing better than all baseline approaches.

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