MIGDroid:通过方法调用图检测APP-Repackaging Android恶意软件

Wenjun Hu, Jing Tao, Xiaobo Ma, Wenyu Zhou, Shuang Zhao, Ting Han
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引用次数: 36

摘要

随着Android平台的日益普及,Android恶意软件,尤其是将恶意代码注入合法Android应用程序的APP-Repackaging恶意软件正在迅速蔓延。本文提出了一个名为MIGDroid的新系统,该系统利用基于方法调用图的静态分析来检测APP-Repackaging Android恶意软件。方法调用图反映了不同方法之间的“交互”连接。由于注入的恶意代码和合法应用程序之间的连接被认为是弱的,因此这种图可以很自然地用于检测应用程序重新包装恶意软件。具体来说,MIGDroid首先在小代码层构造方法调用图,然后将方法调用图划分为弱连通子图。为了确定哪个子图对应注入的恶意代码,根据调用的敏感api计算每个子图的威胁得分,得分越高的子图越有可能是恶意代码。基于真实世界1260个Android恶意软件样本的实验结果证明了我们的系统在检测APP-Repackaging Android恶意软件方面的专长,从而很好地补充了现有的静态分析系统(如Androguard),这些系统不关注APP-Repackaging Android恶意软件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MIGDroid: Detecting APP-Repackaging Android malware via method invocation graph
With the increasing popularity of Android platform, Android malware, especially APP-Repackaging malware wherein the malicious code is injected into legitimate Android applications, is spreading rapidly. This paper proposes a new system named MIGDroid, which leverages method invocation graph based static analysis to detect APP-Repackaging Android malware. The method invocation graph reflects the “interaction” connections between different methods. Such graph can be naturally exploited to detect APP-Repackaging malware because the connections between injected malicious code and legitimate applications are expected to be weak. Specifically, MIGDroid first constructs method invocation graph on the smali code level, and then divides the method invocation graph into weakly connected sub-graphs. To determine which sub-graph corresponds to the injected malicious code, the threat score is calculated for each sub-graph based on the invoked sensitive APIs, and the subgraphs with higher scores will be more likely to be malicious. Experiment results based on 1,260 Android malware samples in the real world demonstrate the specialty of our system in detecting APP-Repackaging Android malware, thereby well complementing existing static analysis systems (e.g., Androguard) that do not focus on APP-Repackaging Android malware.
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