BarkDroid: Android Malware Detection Using Bark Frequency Cepstral Coefficients
| Autor: | Paul Tarwireyi, Alfredo Terzoli, Matthew O. Adigun |
|---|---|
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Indonesian Journal of Information Systems. 5:48-63 |
| ISSN: | 2623-2308 2623-0119 |
| DOI: | 10.24002/ijis.v5i1.6266 |
| Popis: | 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 presents BarkDroid, a novel Android malware detection technique that uses the low-level Bark Frequency Cepstral Coefficients audio features to detect malware. The results obtained outperform results obtained using other features on the same datasets. BarkDroid achieved 97.9% accuracy, 98.5% precision, an F1 score of 98.6%, and shorter execution times. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|