TaintBench: Automatic real-world malware benchmarking of Android taint analyses
Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely docu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Luo, Linghui [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Empirical software engineering - Springer US, 1996, 27(2021), 1 vom: 29. Okt. |
|---|---|
| Übergeordnetes Werk: |
volume:27 ; year:2021 ; number:1 ; day:29 ; month:10 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10664-021-10013-5 |
|---|
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OLC2077302100 |
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| 520 | |a Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. | ||
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10.1007/s10664-021-10013-5 doi (DE-627)OLC2077302100 (DE-He213)s10664-021-10013-5-p DE-627 ger DE-627 rakwb eng 004 VZ Luo, Linghui verfasserin aut TaintBench: Automatic real-world malware benchmarking of Android taint analyses 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. Taint analysis Benchmark Real-world benchmark Android malware Pauck, Felix aut Piskachev, Goran aut Benz, Manuel aut Pashchenko, Ivan aut Mory, Martin aut Bodden, Eric aut Hermann, Ben aut Massacci, Fabio aut Enthalten in Empirical software engineering Springer US, 1996 27(2021), 1 vom: 29. Okt. (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:27 year:2021 number:1 day:29 month:10 https://doi.org/10.1007/s10664-021-10013-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 27 2021 1 29 10 |
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10.1007/s10664-021-10013-5 doi (DE-627)OLC2077302100 (DE-He213)s10664-021-10013-5-p DE-627 ger DE-627 rakwb eng 004 VZ Luo, Linghui verfasserin aut TaintBench: Automatic real-world malware benchmarking of Android taint analyses 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. Taint analysis Benchmark Real-world benchmark Android malware Pauck, Felix aut Piskachev, Goran aut Benz, Manuel aut Pashchenko, Ivan aut Mory, Martin aut Bodden, Eric aut Hermann, Ben aut Massacci, Fabio aut Enthalten in Empirical software engineering Springer US, 1996 27(2021), 1 vom: 29. Okt. (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:27 year:2021 number:1 day:29 month:10 https://doi.org/10.1007/s10664-021-10013-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 27 2021 1 29 10 |
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10.1007/s10664-021-10013-5 doi (DE-627)OLC2077302100 (DE-He213)s10664-021-10013-5-p DE-627 ger DE-627 rakwb eng 004 VZ Luo, Linghui verfasserin aut TaintBench: Automatic real-world malware benchmarking of Android taint analyses 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. Taint analysis Benchmark Real-world benchmark Android malware Pauck, Felix aut Piskachev, Goran aut Benz, Manuel aut Pashchenko, Ivan aut Mory, Martin aut Bodden, Eric aut Hermann, Ben aut Massacci, Fabio aut Enthalten in Empirical software engineering Springer US, 1996 27(2021), 1 vom: 29. Okt. (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:27 year:2021 number:1 day:29 month:10 https://doi.org/10.1007/s10664-021-10013-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 27 2021 1 29 10 |
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10.1007/s10664-021-10013-5 doi (DE-627)OLC2077302100 (DE-He213)s10664-021-10013-5-p DE-627 ger DE-627 rakwb eng 004 VZ Luo, Linghui verfasserin aut TaintBench: Automatic real-world malware benchmarking of Android taint analyses 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2021 Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. Taint analysis Benchmark Real-world benchmark Android malware Pauck, Felix aut Piskachev, Goran aut Benz, Manuel aut Pashchenko, Ivan aut Mory, Martin aut Bodden, Eric aut Hermann, Ben aut Massacci, Fabio aut Enthalten in Empirical software engineering Springer US, 1996 27(2021), 1 vom: 29. Okt. (DE-627)235946516 (DE-600)1401304-6 (DE-576)102432406 1382-3256 nnns volume:27 year:2021 number:1 day:29 month:10 https://doi.org/10.1007/s10664-021-10013-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT AR 27 2021 1 29 10 |
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TaintBench: Automatic real-world malware benchmarking of Android taint analyses |
| abstract |
Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. © The Author(s) 2021 |
| abstractGer |
Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. © The Author(s) 2021 |
| abstract_unstemmed |
Abstract Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors. © The Author(s) 2021 |
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| title_short |
TaintBench: Automatic real-world malware benchmarking of Android taint analyses |
| url |
https://doi.org/10.1007/s10664-021-10013-5 |
| remote_bool |
false |
| author2 |
Pauck, Felix Piskachev, Goran Benz, Manuel Pashchenko, Ivan Mory, Martin Bodden, Eric Hermann, Ben Massacci, Fabio |
| author2Str |
Pauck, Felix Piskachev, Goran Benz, Manuel Pashchenko, Ivan Mory, Martin Bodden, Eric Hermann, Ben Massacci, Fabio |
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235946516 |
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| doi_str |
10.1007/s10664-021-10013-5 |
| up_date |
2024-07-03T14:50:01.269Z |
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7.4013615 |