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Explainable machine learning for malware detection on Android applications

2024Journal article Open access
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dc.contributor.authorPalma, Catarina
dc.contributor.authorJ. Ferreira, Artur
dc.contributor.authorFigueiredo, Mário
dc.date.accessioned2024-03-27T16:47:22Z
dc.date.available2024-03-27T16:47:22Z
dc.date.issued2024
dc.description.abstractThe presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPalma C, Ferreira A, Figueiredo M. Explainable Machine Learning for Malware Detection on Android Applications. Information. 2024; 15(1):25. https://doi.org/10.3390/info15010025pt_PT
dc.identifier.doi10.3390/info15010025pt_PT
dc.identifier.eissn2078-2489
dc.identifier.urihttp://hdl.handle.net/10400.21/17228
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationC645008882-00000055pt_PT
dc.relationThree-way data analysis: computational and statistical challenges, and biomedical applications
dc.relationInstituto de Telecomunicações
dc.relation.publisherversionhttps://www.mdpi.com/2078-2489/15/1/25pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectandroid applicationspt_PT
dc.subjectdatasetspt_PT
dc.subjectexplainabilitypt_PT
dc.subjectfeature selectionpt_PT
dc.subjectmachine learningpt_PT
dc.subjectmalware detectionpt_PT
dc.subjectnumerosity balancing; securitypt_PT
dc.subjectsoft computingpt_PT
dc.subjectsupervised learningpt_PT
dc.titleExplainable machine learning for malware detection on Android applicationspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleThree-way data analysis: computational and statistical challenges, and biomedical applications
oaire.awardTitleInstituto de Telecomunicações
oaire.awardURIinfo:eu-repo/grantAgreement/FCT//SFRH%2FBD%2F145472%2F2019/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT
oaire.citation.endPage25pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleInformationpt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFerreira
person.givenNameArtur
person.identifier1049438
person.identifier.ciencia-id091A-96FB-A88C
person.identifier.orcid0000-0002-6508-0932
person.identifier.ridAAL-4377-2020
person.identifier.scopus-author-id35315359300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isAuthorOfPublication.latestForDiscovery734bfe75-0c68-4cdf-8a87-2aef3564f5bd
relation.isProjectOfPublication545c0ea3-acbb-4ce3-9d81-038d1345b80e
relation.isProjectOfPublication75f6c6d9-5365-4d88-a66f-00492a6ffb5a
relation.isProjectOfPublication.latestForDiscovery75f6c6d9-5365-4d88-a66f-00492a6ffb5a

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