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Journal Article
BarkDroid: Android malware detection using Bark Frequency Cepstral Coefficients
Author(s): Tarwireyi, Paul; Terzoli, Alfredo; Adigun, Matthew O.
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Abstract: Since 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.
Citation: Tarwireyi, 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.
Publication Place: Indonesia
Other: https://doi.org/10.24002/ijis.v5i1.6266
Journal Title: Indonesian Journal of Information Systems (IJIS)
Journal Issue / Part Number: 5 / 1
Peer reviewed: Yes
International Conference: issn
Keynote / Plenary: issn
In Proceedings: issn
ISSN: 2623-2308 (online)
Other: https://doi.org/10.24002/ijis.v5i1.6266
Publisher: Universitas Atma Jaya Yogyakarta
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