DeepBinDiff: Learning Program-Wide Code Representations for Binary Diffing Article Swipe
Yue Duan
,
Xuezixiang Li
,
Jinghan Wang
,
Heng Yin
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.14722/ndss.2020.24311
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.14722/ndss.2020.24311
Binary diffing analysis quantitatively measures the differences between two given binaries and produces fine-grained basic block level matching.It has been widely used to enable different kinds of critical security analysis.However, all existing program analysis and machine learning based techniques suffer from low accuracy, poor scalability, coarse granularity, or require extensive labeled training data to function.In this paper, we propose an unsupervised program-wide code representation learning technique to solve the problem.We rely on both the code semantic information and the program-wide control flow information to generate basic block embeddings.Furthermore, we propose a khop greedy matching algorithm to find the optimal diffing results using the generated block embeddings.We implement a prototype called DEEPBINDIFF and evaluate its effectiveness and efficiency with a large number of binaries.The results show that our tool outperforms the state-of-the-art binary diffing tools by a large margin for both cross-version and cross-optimization-level diffing.A case study for OpenSSL using real-world vulnerabilities further demonstrates the usefulness of our system.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.14722/ndss.2020.24311
- https://doi.org/10.14722/ndss.2020.24311
- OA Status
- gold
- Cited By
- 159
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3007413911
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3007413911Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.14722/ndss.2020.24311Digital Object Identifier
- Title
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DeepBinDiff: Learning Program-Wide Code Representations for Binary DiffingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-01-01Full publication date if available
- Authors
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Yue Duan, Xuezixiang Li, Jinghan Wang, Heng YinList of authors in order
- Landing page
-
https://doi.org/10.14722/ndss.2020.24311Publisher landing page
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https://doi.org/10.14722/ndss.2020.24311Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.14722/ndss.2020.24311Direct OA link when available
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Computer science, Programming language, Code (set theory), Binary number, Program comprehension, Binary code, Natural language processing, Artificial intelligence, Arithmetic, Mathematics, Software, Software system, Set (abstract data type)Top concepts (fields/topics) attached by OpenAlex
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159Total citation count in OpenAlex
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2025: 26, 2024: 33, 2023: 38, 2022: 31, 2021: 26Per-year citation counts (last 5 years)
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67Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.number | 120 |
| abstract_inverted_index.paper, | 56 |
| abstract_inverted_index.suffer | 39 |
| abstract_inverted_index.widely | 20 |
| abstract_inverted_index.OpenSSL | 147 |
| abstract_inverted_index.between | 7 |
| abstract_inverted_index.control | 80 |
| abstract_inverted_index.diffing | 1, 99, 132 |
| abstract_inverted_index.further | 151 |
| abstract_inverted_index.labeled | 50 |
| abstract_inverted_index.machine | 35 |
| abstract_inverted_index.optimal | 98 |
| abstract_inverted_index.program | 32 |
| abstract_inverted_index.propose | 58, 89 |
| abstract_inverted_index.require | 48 |
| abstract_inverted_index.results | 100, 123 |
| abstract_inverted_index.system. | 157 |
| abstract_inverted_index.analysis | 2, 33 |
| abstract_inverted_index.binaries | 10 |
| abstract_inverted_index.critical | 27 |
| abstract_inverted_index.evaluate | 112 |
| abstract_inverted_index.existing | 31 |
| abstract_inverted_index.generate | 84 |
| abstract_inverted_index.learning | 36, 64 |
| abstract_inverted_index.matching | 93 |
| abstract_inverted_index.measures | 4 |
| abstract_inverted_index.produces | 12 |
| abstract_inverted_index.security | 28 |
| abstract_inverted_index.semantic | 75 |
| abstract_inverted_index.training | 51 |
| abstract_inverted_index.accuracy, | 42 |
| abstract_inverted_index.algorithm | 94 |
| abstract_inverted_index.different | 24 |
| abstract_inverted_index.diffing.A | 143 |
| abstract_inverted_index.extensive | 49 |
| abstract_inverted_index.generated | 103 |
| abstract_inverted_index.implement | 106 |
| abstract_inverted_index.prototype | 108 |
| abstract_inverted_index.technique | 65 |
| abstract_inverted_index.efficiency | 116 |
| abstract_inverted_index.problem.We | 69 |
| abstract_inverted_index.real-world | 149 |
| abstract_inverted_index.techniques | 38 |
| abstract_inverted_index.usefulness | 154 |
| abstract_inverted_index.DEEPBINDIFF | 110 |
| abstract_inverted_index.differences | 6 |
| abstract_inverted_index.function.In | 54 |
| abstract_inverted_index.information | 76, 82 |
| abstract_inverted_index.matching.It | 17 |
| abstract_inverted_index.outperforms | 128 |
| abstract_inverted_index.binaries.The | 122 |
| abstract_inverted_index.demonstrates | 152 |
| abstract_inverted_index.fine-grained | 13 |
| abstract_inverted_index.granularity, | 46 |
| abstract_inverted_index.program-wide | 61, 79 |
| abstract_inverted_index.scalability, | 44 |
| abstract_inverted_index.unsupervised | 60 |
| abstract_inverted_index.cross-version | 140 |
| abstract_inverted_index.effectiveness | 114 |
| abstract_inverted_index.embeddings.We | 105 |
| abstract_inverted_index.quantitatively | 3 |
| abstract_inverted_index.representation | 63 |
| abstract_inverted_index.vulnerabilities | 150 |
| abstract_inverted_index.state-of-the-art | 130 |
| abstract_inverted_index.analysis.However, | 29 |
| abstract_inverted_index.embeddings.Furthermore, | 87 |
| abstract_inverted_index.cross-optimization-level | 142 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.99503229 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |