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arXiv · 2205.06719

dewolf: Improving Decompilation by leveraging User Surveys

Steffen Enders·Eva-Maria C. Behner·Niklas Bergmann·Mariia Rybalka·Elmar Padilla·Er Xue Hui·Henry Low·Nicholas Sim
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Abstract

Analyzing third-party software such as malware or firmware is a crucial task for security analysts. Although various approaches for automatic analysis exist and are the subject of ongoing research, analysts often have to resort to manual static analysis to get a deep understanding of a given binary sample. Since the source code of encountered samples is rarely available, analysts regularly employ decompilers for easier and faster comprehension than analyzing a binary's disassembly. In this paper, we introduce our decompilation approach dewolf. We developed a variety of improvements over the previous academic state-of-the-art decompiler and some novel algorithms to enhance readability and comprehension, focusing on manual analysis. To evaluate our approach and to obtain a better insight into the analysts' needs, we conducted three user surveys. The results indicate that dewolf is suitable for malware comprehension and that its output quality noticeably exceeds Ghidra and Hex-Rays in certain aspects. Furthermore, our results imply that decompilers aiming at manual analysis should be highly configurable to respect individual user preferences. Additionally, future decompilers should not necessarily follow the unwritten rule to stick to the code-structure dictated by the assembly in order to produce readable output. In fact, the few cases where dewolf already cracks this rule lead to its results considerably exceeding other decompilers. We publish a prototype implementation of dewolf and all survey results on GitHub.

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Steffen Enders, Eva-Maria C. Behner, Niklas Bergmann, Mariia Rybalka, Elmar Padilla, Er Xue Hui, Henry Low, Nicholas Sim. 2022-05-13. dewolf: Improving Decompilation by leveraging User Surveys. https://doi.org/10.14722/bar.2023.23001

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