Jean-Marie 'Histausse' Mineau
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34 lines
2.3 KiB
Typst
34 lines
2.3 KiB
Typst
#import "@local/template-thesis-matisse:0.0.1": etal
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#import "../lib.typ": todo, jfl-note
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#import "../lib.typ": pb1, pb1-text, APKs, SDK, highlight-block
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#import "X_var.typ": *
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== Conclusion <sec:rasta-conclusion>
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Since the release of Android, many tools have been published in order to analyse Android applications.
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In @sec:bg, we went through contributions that benchmark and compare some of those tools.
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Those contributions suggested that analysing real-world applications might be more challenging than expected.
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This led us to question the reusability of those tools (#pb1).
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This chapter has assessed the suggested results of the literature~@luoTaintBenchAutomaticRealworld2022 @pauckAndroidTaintAnalysis2018 @reaves_droid_2016 about the reliability of static analysis tools for Android applications.
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With a dataset of #NBTOTALSTRING applications, we established that #resultunusable of #nbtoolsselectedvariations tools are not reusable.
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2 of those were due to the fact that we did not manage to use the tools, even with the help of the author.
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We consider the 10 other tools to be unusable due to the fact that they fail to finish their analysis more than 50% of the time..
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In total, the analysis success rate of the tools that we could run for the entire dataset is #resultratio.
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The characteristics that have the most influence on the success rate are the bytecode size and the min #SDK version.
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Finally, we showed that malware #APKs generate fewer fatal errors than goodware when analysed.
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Following Reaves #etal recommendations~@reaves_droid_2016, we publish the Docker and Singularity images we built to run our experiments alongside the Docker files.
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This will allow the research community to use the tools directly without the build and installation penalty.
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#v(2em)
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#align(center, highlight-block(inset: 15pt, width: 75%, breakable: false, block(align(left)[
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#pb1: #pb1-text
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#v(0.75em)
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More than half the tools we selected were not usable.
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In some cases, it was due to our inability to set up the tool correctly.
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Mostly, it was due to the high failure rate when analysing real-world applications.
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Results show that large applications cause more crashes, as do applications with a higher min #SDK target.
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Goodware also appear to generate more analysis failures than malware.
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])))
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