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On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection

Authors:

Tegawendé F. Bissyandé,

John GrundyAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 30, Issue 3

Article No.: 40, Pages 1 - 38

https://doi.org/10.1145/3446905

Published: 08 May 2021 Publication History

Abstract

Malware detection at scale in the Android realm is often carried out using machine learning techniques. State-of-the-art approaches such as DREBIN and MaMaDroid are reported to yield high detection rates when assessed against well-known datasets. Unfortunately, such datasets may include a large portion of duplicated samples, which may bias recorded experimental results and insights. In this article, we perform extensive experiments to measure the performance gap that occurs when datasets are de-duplicated. Our experimental results reveal that duplication in published datasets has a limited impact on supervised malware classification models. This observation contrasts with the finding of Allamanis on the general case of machine learning bias for big code. Our experiments, however, show that sample duplication more substantially affects unsupervised learning models (e.g., malware family clustering). Nevertheless, we argue that our fellow researchers and practitioners should always take sample duplication into consideration when performing machine-learning-based (via either supervised or unsupervised learning) Android malware detections, no matter how significant the impact might be.

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Index Terms

On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software notations and tools

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Published In

cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 30, Issue 3

Continuous Special Section: AI and SE

July 2021

600 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3450566

Editor:
Mauro Pezzè
Università della Svizzera italiana and Università di Milano-Bicocca, Switzerland

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Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 08 May 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 March 2020

Published in TOSEM Volume 30, Issue 3

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Discovery Early Career Researcher Award (DECRA)
European Union's Horizon 2020 research and innovation program
National Natural Science Foundation of China
Discovery project
Fonds National de la Recherche (FNR), Luxembourg, under project CHARACTERIZE
SPARTA project
Australian Research Council (ARC) under a Laureate Fellowship

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https://dl.acm.org/doi/10.61822/amcs-2024-0021
Huckvale EMoseley H(2024)A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvementPLOS ONE10.1371/journal.pone.029958319:5(e0299583)Online publication date: 2-May-2024
https://doi.org/10.1371/journal.pone.0299583
Lachance AMosser SCombemale BWimmer MChechik MEgyed A(2024)Building deduplicated model repositories to assess domain-specific languages evolutionProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688338(1025-1034)Online publication date: 22-Sep-2024
https://dl.acm.org/doi/10.1145/3652620.3688338
Gao CYang WYe JXue YSun J(2024)sGuard+: Machine Learning Guided Rule-Based Automated Vulnerability Repair on Smart ContractsACM Transactions on Software Engineering and Methodology10.1145/364184633:5(1-55)Online publication date: 4-Jun-2024
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Kurniawan FStiawan DAntoni DHeriyanto AIdris MBudiarto R(2024)Hybrid Machine Learning Model for Anticipating Cyber Crime Malware in Android: Work on Progress2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)10.1109/EECSI63442.2024.10776359(499-505)Online publication date: 26-Sep-2024
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Maray MMaashi MAlshahrani HAljameel SAbdelbagi SSalama A(2024)Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware DetectionIEEE Access10.1109/ACCESS.2024.335794412(24516-24524)Online publication date: 2024
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Jeremiah SChen HGritzalis SPark J(2024)Leveraging application permissions and network traffic attributes for Android ransomware detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.103950230:COnline publication date: 18-Oct-2024
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Zhao YZhang YWang HLin ZLiao X(2023)Potential Risks Arising from the Absence of Signature Verification in Miniapp PluginsProceedings of the 2023 ACM Workshop on Secure and Trustworthy Superapps10.1145/3605762.3624433(59-64)Online publication date: 26-Nov-2023
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Sun XChen XLi LCai HGrundy JSamhi JBissyandé TKlein J(2023)Demystifying Hidden Sensitive Operations in Android AppsACM Transactions on Software Engineering and Methodology10.1145/357415832:2(1-30)Online publication date: 29-Mar-2023
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