research-article

sem2vec: Semantics-aware Assembly Tracelet Embedding

Authors:

Shi WuAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology, Volume 32, Issue 4

Article No.: 90, Pages 1 - 34

https://doi.org/10.1145/3569933

Published: 27 May 2023 Publication History

Abstract

Binary code similarity is the foundation of many security and software engineering applications. Recent works leverage deep neural networks (DNN) to learn a numeric vector representation (namely, embeddings) of assembly functions, enabling similarity analysis in the numeric space. However, existing DNN-based techniques capture syntactic-, control flow-, or data flow-level information of assembly code, which is too coarse-grained to represent program functionality. These methods can suffer from low robustness to challenging settings such as compiler optimizations and obfuscations.

We present sem2vec, a binary code embedding framework that learns from semantics. Given the control-flow graph (CFG), 34 pages. of an assembly function, we divide it into tracelets, denoting continuous and short execution traces that are reachable from the function entry point. We use symbolic execution to extract symbolic constraints and other auxiliary information on each tracelet. We then train masked language models to compute embeddings of symbolic execution outputs. Last, we use graph neural networks, to aggregate tracelet embeddings into the CFG-level embedding for a function. Our evaluation shows that sem2vec extracts high-quality embedding and is robust against different compilers, optimizations, architectures, and popular obfuscation methods including virtualization obfuscation. We further augment a vulnerability search application with embeddings computed by sem2vec and demonstrate a significant improvement in vulnerability search accuracy.

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Zhang YFang BXiong ZWang YLiu YZheng CZhang Q(2024)A Semantics-Based Approach on Binary Function Similarity DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.338901411:15(25910-25924)Online publication date: 1-Aug-2024
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Index Terms

sem2vec: Semantics-aware Assembly Tracelet Embedding
1. Security and privacy
  1. Software and application security
    1. Software security engineering

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cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 32, Issue 4

July 2023

938 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3599692

Editor:
Mauro Pezzè
USI Università della Svizzera italiana and SIT Schaffhausen Institute of Technology, Switzerland

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Publication History

Published: 27 May 2023

Online AM: 28 October 2022

Accepted: 06 October 2022

Revised: 21 September 2022

Received: 09 February 2022

Published in TOSEM Volume 32, Issue 4

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Cited By

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https://doi.org/10.1145/3597503.3623328
Zhang YFang BXiong ZWang YLiu YZheng CZhang Q(2024)A Semantics-Based Approach on Binary Function Similarity DetectionIEEE Internet of Things Journal10.1109/JIOT.2024.338901411:15(25910-25924)Online publication date: 1-Aug-2024
https://doi.org/10.1109/JIOT.2024.3389014
Wang HLiu ZWang SWang YTang QNie SWu S(2024)Are We There Yet? Filling the Gap Between Binary Similarity Analysis and Binary Software Composition Analysis2024 IEEE 9th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP60621.2024.00034(506-523)Online publication date: 8-Jul-2024
https://doi.org/10.1109/EuroSP60621.2024.00034
Zhang PWu CWang Z(2024)BinCodex: A comprehensive and multi-level dataset for evaluating binary code similarity detection techniquesBenchCouncil Transactions on Benchmarks, Standards and Evaluations10.1016/j.tbench.2024.1001634:2(100163)Online publication date: Jun-2024
https://doi.org/10.1016/j.tbench.2024.100163
Liu LWang SJiang X(2024)UniBin: Assembly Semantic-enhanced Binary Vulnerability Detection without DisassemblyInformation Sciences10.1016/j.ins.2024.121605(121605)Online publication date: Oct-2024
https://doi.org/10.1016/j.ins.2024.121605

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