Dexpler

Bartel, Alexandre; Klein, Jacques; Traon, Yves Le; Monperrus, Martin

doi:10.1145/2259051.2259056

Cited by 165 publications

(10 citation statements)

References 17 publications

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“…Android API packages, noted PAPI. These features are extracted using Dexpler (Bartel et al 2012) and Soot (Vallée-Rai et al 1999); -Reflective Feature: RevealDroid extracts information about dynamic class loading that is represented by: the full or partial method names invoked; the number of times the full or partial method name is invoked; and the total number of reflective invocations. Note that full method names are defined by the original authors as the methods that have both the reflectively invoked method and class names statically determined, and the partial method names are the ones that have only the invoked method name statically determined.…”

Section: Virustotalmentioning

confidence: 99%

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

et al. 2021

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A well-known curse of computer security research is that it often produces systems that, while technically sound, fail operationally. To overcome this curse, the community generally seeks to assess proposed systems under a variety of settings in order to make explicit every potential bias. In this respect, recently, research achievements on machine learning based malware detection are being considered for thorough evaluation by the community. Such an effort of comprehensive evaluation supposes first and foremost the possibility to perform an independent reproduction study in order to sharpen evaluations presented by approaches’ authors. The question Can published approaches actually be reproduced? thus becomes paramount despite the little interest such mundane and practical aspects seem to attract in the malware detection field. In this paper, we attempt a complete reproduction of five Android Malware Detectors from the literature and discuss to what extent they are “reproducible”. Notably, we provide insights on the implications around the guesswork that may be required to finalise a working implementation. Finally, we discuss how barriers to reproduction could be lifted, and how the malware detection field would benefit from stronger reproducibility standards—like many various fields already have.

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Section: Virustotalmentioning

confidence: 99%

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

et al. 2021

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“…Alternative approaches to Dalvik instrumentation focus on performing detours via other languages, e.g., Java or Jimple. For example, Bartel et al [9] worked on instrumenting Android apps for improving their privacy and security via translation to Java bytecode. Zhauniarovich et al [81] translated Dalvik into Java bytecode in order to use EMMA's code coverage measurement functionality, while Horvath et al [33] used translation into Java bytecode to use their own JInstrumenter library for jar files instrumentation.…”

Section: Coverage Measurement Tools In Androidmentioning

confidence: 99%

Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Pilgun

Gadyatskaya

Zhauniarovich³

et al. 2020

ACM Trans. Softw. Eng. Methodol.

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Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community. Code coverage is one of the most common metrics for evaluating effectiveness of these frameworks. Furthermore, code coverage is used as a fitness function for guiding evolutionary and fuzzy testing techniques. However, there are no reliable tools for measuring fine-grained code coverage in black-box Android app testing. We present the Android Code coVerage Tool, ACVTool for short, that instruments Android apps and measures code coverage in the black-box setting at class, method and instruction granularity. ACVTool has successfully instrumented 96.9% of apps in our experiments. It introduces a negligible instrumentation time overhead, and its runtime overhead is acceptable for automated testing tools. We demonstrate practical value of ACVTool in a large-scale experiment with Sapienz, a state-of-the-art automated testing tool. Using ACVTool on the same cohort of apps, we have compared different coverage granularities applied by Sapienz in terms of the found amount of crashes. Our results show that none of the applied coverage granularities clearly outperforms others in this aspect.

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“…Bytecode instrumentation is commonly used to modify the behavior of Java applications [24] . It consists in modifying the binary code generated by the compiler (e.g.,.class files).…”

Section: Bytecode Instrumentationmentioning

confidence: 99%

Energy efficient adaptation engines for android applications

Cañete

Horcas

Ayala

et al. 2020

Information and Software Technology

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ContextThe energy consumption of mobile devices is increasing due to the improvement in their components (e.g., better processors, larger screens). Although the hardware consumes the energy, the software is responsible for managing hardware resources such as the camera software and its functionality, and therefore, affects the energy consumption. Energy consumption not only depends on the installed code, but also on the execution context (environment, devices status) and how the user interacts with the application.Objective In order to reduce the energy consumption based on user behavior, it is necessary to dynamically adapt the application. However, the adaptation mechanism also consumes a certain amount of energy in itself, which may lead to an important increase in the energy expenditure of the application in comparison with the benefits of the adaptation. Therefore, this footprint must be measured and compared with the benefit obtained.Method In this paper, we (1) determine the benefits, in terms of energy consumption, of dynamically adapting mobile applications, based on user behavior; and (2) advocate the most energy-efficient adaptation mechanism. We provide four different implementations of a proposed adaptation model and measure their energy consumption. ResultsThe proposed adaptation engines do not increase the energy consumption when compared to the benefits of the adaptation, which can reduce the energy consumption by up to 20%. ConclusionThe adaptation engines proposed in this paper can decrease the energy consumption of the mobile devices based on user behavior. The overhead introduced by the adaptation engines is negligible in comparison with the benefits obtained by the adaptation.

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Dexpler

Cited by 165 publications

References 17 publications

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection

Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Energy efficient adaptation engines for android applications

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