用 DOOLDA 对受反分析技术保护的安卓恶意软件进行混合动态分析

Sunjun Lee Sunjun Lee, Yonggu Shin Sunjun Lee, Minseong Choi Yonggu Shin, Haehyun Cho Minseong Choi, Jeong Hyun Yi Haehyun Cho
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引用次数: 0

摘要

最近报告的许多恶意软件都配备了反分析技术(如反仿真、反调试等),以防止被分析,这可能会延迟检测并使恶意软件存活更长时间。因此,开发自动方法来破解这些反分析技术,以便我们能够处理和有效缓解众多恶意软件,就显得尤为重要。本文通过分析 1,535 个恶意应用程序,发现其中 18.31% 的应用程序配备了反分析技术。接下来,我们提出了一种名为 DOOLDA 的新型动态分析器,用于通过动态仪器自动失效反分析技术。DOOLDA 监控 Android 应用程序整个代码层(即字节码和本地代码)的执行情况。根据监控结果,DOOLDA 找到与反分析技术相关的代码,并通过仪器化使反分析技术失效。为了证明 DOOLDA 的有效性,我们展示了它可以使所有已知的反分析技术失效。此外,我们还将 DOOLDA 与其他动态分析器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid Dynamic Analysis for Android Malware Protected by Anti-Analysis Techniques with DOOLDA
A lot of the recently reported malware is equipped with the anti-analysis techniques (e.g., anti-emulation, anti-debugging, etc.) for preventing from being the analyzed, which can delay detection and make malware alive for a longer period. Therefore, it is of the great importance of developing automated approaches to defeat such anti-analysis techniques so that we can handle and effectively mitigate numerous malware. In this paper, by analyzing 1,535 malicious applications, we found that 18.31% of them equipped with anti-analysis techniques. Next, we propose a novel, dynamic analyzer, named DOOLDA, for automatically invalidating anti-analysis techniques through dynamic instrumentation. DOOLDA monitors executions of Android applications’ entire code layers (i.e., bytecode and native code). Based on monitoring results, DOOLDA finds the code related to anti-analysis techniques and invalidates the anti-analysis techniques by instrumenting it. To demonstrate the effectiveness of DOOLDA, we show that it can invalidate all known anti-analysis techniques. Also, we compare DOOLDA with other dynamic analyzers.  
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