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CCS2018顶会

Towards Paving the Way for Large-Scale Windows Malware Analysis: Generic Binary Unpacking with Orders-of-Magnitude Performance Boost

Binlin Cheng, Jiang Ming, Jianming Fu, Guojun Peng, Ting Chen, Xiaosong Zhang, Jean-Yves Marion

2018年份
68被引次数
16顶会引用

摘要

Binary packing, encoding binary code prior to execution and decoding them at run time, is the most common obfuscation adopted by malware authors to camouflage malicious code. Especially, most packers recover the original code by going through a set of "writtenthen-executed" layers, which renders determining the end of the unpacking increasingly difficult. Many generic binary unpacking approaches have been proposed to extract packed binaries without the prior knowledge of packers. However, the high runtime overhead and lack of anti-analysis resistance have severely limited their adoptions. Over the past two decades, packed malware is always a veritable challenge to anti-malware landscape. This paper revisits the long-standing binary unpacking problem from a new angle: packers consistently obfuscate the standard use of API calls. Our in-depth study on an enormous variety of Windows malware packers at present leads to a common property: malware's Import Address Table (IAT), which acts as a lookup table for dynamically linked API calls, is typically erased by packers for further obfuscation; and then unpacking routine, like a custom * Both authors contributed equally to the paper.

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