.png){kind=icon}
.png){kind=icon}
Return
Function Representations for Binary Similarity
.png){kind=icon}
OA
AI
DOI：10.1109/TDSC.2021.3051852.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 article 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 previous solutions. Furthermore, we show how SAFE can be used in widely different use cases, thus providing a general solution for several application scenarios.
Keywords:
Task analysis
Malware
Computer architecture
Semantics
Binary codes
Training
Manuals
Binary analysis
binary similarity
deep learning
malware
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.4K
Citations:
9.6K