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Dr 

Tarwireyi, Paul

Faculty: Faculty of Science, Agriculture and Engineering
Department: Computer Science
ORCID: https://orcid.org/0000-0001-6625-3826
Research Interest(s): Information security, Machine learning, Computer networks, and Cloud computing.
Active Community Engagement: Institute of Electrical and Electronics Engineers (IEEE) Institute of Information Technology Professionals South Africa (IITPSA) South African Institute of Computer Scientists and Information Technologists (SAICSIT)
Biography: Paul Tarwireyi is a lecturer at the Department of Computer Science. He holds a Bachelor of Science, MSc from University of Fort Hare and his B.Sc. Honours Degree from Rhodes University. His research interests are in Information security, computer networks and Service Oriented Computing.
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20221
Subject: Android malware detection ×
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    BarkDroid: Android malware detection using Bark Frequency Cepstral Coefficients
    2022
     | Universitas Atma Jay...
    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.
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