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| Content Provider | ACM Digital Library |
|---|---|
| Author | Groz, Roland de Goër, Franck Mounier, Laurent |
| Abstract | We present an approach to recover information on function signatures and data-flow relations from stripped binary code. Contrary to most approaches based either on static analysis or fine-grained dynamic instrumentation, we propose lightweight instrumentation and heuristics. Our goal is to get a fast and scalable pre-processing that could serve as a front-end to focus more detailed analysis of particular functions. We infer arity and parameter types, as well as a coupling relation (which we define). We are interested in particular in couples of functions with a data-flow relation, such as memory allocators. We trade-off accuracy for scalability and performance, but our experiments show that the results of the proposed heuristics can be quite accurate, even on a single random execution. |
| Starting Page | 1 |
| Ending Page | 12 |
| Page Count | 12 |
| File Format | |
| ISBN | 9781450336420 |
| DOI | 10.1145/2843859.2843861 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-12-08 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Dynamic type inference Data-flow Reverse-engineering |
| Content Type | Text |
| Resource Type | Article |
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