BarkDroid: Android malware detection using Bark Frequency Cepstral Coefficients

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dc.contributor.authorTarwireyi, Paul
dc.contributor.authorTerzoli, Alfredo
dc.contributor.authorAdigun, Matthew O.
dc.coverageIndonesia
dc.coverage.conferenceissn
dc.date.accessioned2026-02-24T07:50:48Z
dc.date.available2026-02-24T07:50:48Z
dc.date.issued2022
dc.departmentNameComputer Science
dc.description.abstractSince their inaugural releases in 2007, Google’s Android and Apple’s iOS have grown to dominate the mobile OS market share. Currently, they jointly possess over 99% of the global market share with Android being the leading mobile Operating System of choice worldwide, controlling close to 70% of the market share. Mobile devices have enabled the exponential growth of a plethora of mobile applications that play key roles in enabling many use cases that are pivotal in our daily lives. On the other hand, access to a large pool of potential end users is available to both legitimate and nefarious applications, thus making mobile devices a burgeoning target of malicious applications. Current malware detection solutions rely on tedious, time-consuming, knowledge-based, and manual processes to identify malware. This paper introduces BarkDroid, a novel Android malware detection technique that uses the low-level Bark Frequency Cepstral Coefficients audio features to detect malware. The initial results obtained show that Bark Frequency Cepstral Coefficient shave high discriminative capabilities to achieve accurate preditions. BarkDroid achieved 97.9% accuracy, 98.5% precision, an F1 score of 98.6%,and shorter execution times.
dc.facultyFaculty of Science, Agriculture and Engineering
dc.identifier.citationTarwireyi, P., Terzoli, A. and Adigun, M.O. 2022. BarkDroid: Android malware detection using Bark Frequency Cepstral Coefficients. Indonesian Journal of Information Systems, 5(1), pp.48-63.
dc.identifier.issn2623-2308 (online)
dc.identifier.issn2623-0119 (print)
dc.identifier.otherhttps://doi.org/10.24002/ijis.v5i1.6266
dc.identifier.urihttp://hdl.handle.net/10530/58817
dc.inproceedingsissn
dc.issuenumber5 / 1
dc.keynoteissn
dc.language.isoen
dc.pages48 - 63
dc.peerreviewedYes
dc.publisherUniversitas Atma Jaya Yogyakarta
dc.subjectAndroid malware detection
dc.subjectMalware classification
dc.subjectBark Frequency Cepstral
dc.titleBarkDroid: Android malware detection using Bark Frequency Cepstral Coefficients
dc.title.journalIndonesian Journal of Information Systems (IJIS)
dc.typeJournal Article
dspace.entity.typePublication
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