Function Representations for Binary Similarity

The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection,...

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Bibliographic Details
Published in:IEEE transactions on dependable and secure computing 2022-07, Vol.19 (4), p.1-1
Main Authors: Massarelli, Luca, Di Luna, Giuseppe Antonio, Petroni, Fabio, Querzoni, Leonardo, Baldoni, Roberto
Format: Article
Language:English
Subjects:
Binary Analysis
Binary codes
Binary Similarity
Computer architecture
Deep Learning
Feature extraction
Malware
Manuals
Neural networks
Representations
Semantics
Similarity
Task analysis
Training
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Summary:The binary similarity problem consists in determining if two functions are similar considering only their compiled form. Advanced techniques for binary similarity recently gained momentum as they can be applied in several fields, such as copyright disputes, malware analysis, vulnerability detection, etc. In this paper we describe SAFE, a novel architecture for function representation based on a self-attentive neural network. SAFE works directly on disassembled binary functions, does not require manual feature extraction, is computationally more efficient than existing solutions, and is more general as it works on stripped binaries and on multiple architectures. Results from our experimental evaluation show how SAFE provides a performance improvement with respect to previoussolutions. Furthermore, we show how SAFE can be used in widely different use cases, thus providing a general solution for several application scenarios.
ISSN:1545-5971
1941-0018
DOI:10.1109/TDSC.2021.3051852