Obfuscation-Resilient Executable Payload Extraction From Packed Malware
Binlin Cheng, Jiang Ming, Erika A. Leal, Haotian Zhang, Jianming Fu, Guojun Peng, Jean-Yves Marion
摘要
Over the past two decades, packed malware is always a veritable challenge to security analysts. Not only is determining the end of the unpacking increasingly difficult, but also advanced packers embed a variety of anti-analysis tricks to impede reverse engineering. As malware's APIs provide rich information about malicious behavior, one common anti-analysis strategy is API obfuscation, which removes the metadata of imported APIs from malware's PE header and complicates API name resolution from API callsites. In this way, even when security analysts obtain the unpacked code, a disassembler still fails to recognize imported API names, and the unpacked code cannot be successfully executed. Recently, generic binary unpacking has made breakthrough progress with noticeable performance improvement. However, reconstructing unpacked code's import tables, which is vital for further malware static/dynamic analyses, has largely been overlooked. Existing approaches are far from mature: they either can be easily evaded by various API obfuscation schemes (e.g., stolen code), or suffer from incomplete API coverage. In this paper, we aim to achieve the ultimate goal of Windows malware unpacking: recovering an executable malware program from the packed and obfuscated binary code. Based on the process memory when the original entry point (OEP) is reached, we develop a hardware-assisted tool, API-Xray, to reconstruct import tables. Import table reconstruction is challenging enough in its own right. Our core technique, API Micro Execution, explores all possible API callsites and executes them without knowing API argument values. At the same time, we take advantage of hardware tracing via Intel Branch Trace Store and NX bit to resolve API names and finally rebuild import tables. Compared with the previous work, API-Xray has a better resistance against various API
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引用它的顶会 Paper9
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- Understanding Linux MalwareEmanuele Cozzi, Mariano Graziano, Yanick Fratantonio, Davide BalzarottiS&P 2018 · 被引用 203 次
- BinSim: Trace-based Semantic Binary Diffing via System Call Sliced Segment Equivalence CheckingJiang Ming, Dongpeng Xu, Yufei Jiang, Dinghao WuUSENIX Security 2017 · 被引用 118 次
- Towards Paving the Way for Large-Scale Windows Malware Analysis: Generic Binary Unpacking with Orders-of-Magnitude Performance BoostBinlin Cheng, Jiang Ming, Jianming Fu, Guojun Peng 等CCS 2018 · 被引用 68 次
- Prevalence and Impact of Low-Entropy Packing Schemes in the Malware EcosystemAlessandro Mantovani, Simone Aonzo, Xabier Ugarte-Pedrero, Alessio Merlo 等NDSS 2020
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