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Article: NDroid: Toward tracking information flows across multiple android contexts

TitleNDroid: Toward tracking information flows across multiple android contexts
Authors
KeywordsJava native interface (JNI)
Android application analysis
taint analysis
Issue Date2019
Citation
IEEE Transactions on Information Forensics and Security, 2019, v. 14, n. 3, p. 814-828 How to Cite?
AbstractFor 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.
Persistent Identifierhttp://hdl.handle.net/10722/303579
ISSN
2020 Impact Factor: 7.178
2020 SCImago Journal Rankings: 1.613

 

DC FieldValueLanguage
dc.contributor.authorXue, Lei-
dc.contributor.authorQian, Chenxiong-
dc.contributor.authorZhou, Hao-
dc.contributor.authorLuo, Xiapu-
dc.contributor.authorZhou, Yajin-
dc.contributor.authorShao, Yuru-
dc.contributor.authorChan, Alvin T.S.-
dc.date.accessioned2021-09-15T08:25:36Z-
dc.date.available2021-09-15T08:25:36Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Information Forensics and Security, 2019, v. 14, n. 3, p. 814-828-
dc.identifier.issn1556-6013-
dc.identifier.urihttp://hdl.handle.net/10722/303579-
dc.description.abstractFor 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.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Information Forensics and Security-
dc.subjectJava native interface (JNI)-
dc.subjectAndroid application analysis-
dc.subjecttaint analysis-
dc.titleNDroid: Toward tracking information flows across multiple android contexts-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TIFS.2018.2866347-
dc.identifier.scopuseid_2-s2.0-85052712721-
dc.identifier.volume14-
dc.identifier.issue3-
dc.identifier.spage814-
dc.identifier.epage828-

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