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NDroid: Toward Tracking Information Flows Across Multiple Android Contexts

delete2019-03-01
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薛磊 封面图
薛磊 (Lei Xue) *
C
Chenxiong Qian
H
Hao Zhou
X
Xiapu Luo
Y
Yajin Zhou
Y
Yuru Shao
A
Alvin Chan
DOI:10.1109/TIFS.2018.2866347delete
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摘要

摘要

En 中文
For performance and compatibility reasons, developers tend to use native code in their applications (or simply apps). This makes a bidirectional data flow through multiple contexts, i.e., the Java context and the native context, in Android apps. Unfortunately, this interaction brings serious challenges to existing dynamic analysis systems, which fail to capture the data flow across different contexts. In this paper, we first performed a large-scale study on apps using native code and reported some observations. Then, we identified several scenarios where data flow cannot be tracked by existing systems, leading to uncaught information leakage. Based on these insights, we designed and implemented NDroid, an efficient dynamic taint analysis system that could track the data flow between both Java context and native context. The evaluation of real apps demonstrated the effectiveness of NDroid in identifying information leakage with reasonable performance overhead.
Keyword:
Android application analysis
taint analysis
Java native interface (JNI)
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期刊

IEEE Transactions on Information Forensics and Security 封面图
IEEE Transactions on Information Forensics and Security
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Georgia Institute of Technology
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hong kong polytechnic university
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university of michigan system
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