Article (Scientific journals)
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Daoudi, Nadia; Allix, Kevin; Bissyande, Tegawendé François D Assise et al.
2021In Empirical Software Engineering, 26
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Keywords :
Android malware detection; Reproducibility; Replicability; Machine learning
Abstract :
[en] 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.
Disciplines :
Computer science
Author, co-author :
Daoudi, Nadia ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Allix, Kevin ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Bissyande, Tegawendé François D Assise  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
Klein, Jacques ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > TruX
External co-authors :
no
Language :
English
Title :
Lessons Learnt on Reproducibility in Machine Learning Based Android Malware Detection
Publication date :
2021
Journal title :
Empirical Software Engineering
ISSN :
1573-7616
Publisher :
Springer, United States
Volume :
26
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Security, Reliability and Trust
FnR Project :
FNR11693861 - Characterization Of Malicious Code In Mobile Apps: Towards Accurate And Explainable Malware Detection, 2017 (01/06/2018-31/12/2021) - Jacques Klein
Funders :
FNR - Fonds National de la Recherche [LU]
University of Luxembourg - UL
SPARTA
Luxembourg Ministry of Foreign and European Affairs
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since 27 May 2021

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