Publication:
A New Mobile Botnet Classification based on Permission and API Calls

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dc.ConferencedateSEP 06-08, 2017
dc.ConferencelocationCanterbury, ENGLAND
dc.Conferencename7th International Conference on Emerging Security Technologies (EST)
dc.contributor.authorYusof, Men_US
dc.contributor.authorSaudi, MMen_US
dc.contributor.authorRidzuan, Fen_US
dc.date.accessioned2024-05-29T03:26:35Z
dc.date.available2024-05-29T03:26:35Z
dc.date.issued2017
dc.description.abstractCurrently, mobile botnet attacks have shifted from computers to smartphones due to its functionality, ease to exploit, and based on financial intention. Mostly, it attacks Android due to its popularity and high usage among end users. Every day, more and more malicious mobile applications (apps) with the botnet capability have been developed to exploit end users' smartphones. Therefore, this paper presents a new mobile botnet classification based on permission and Application Programming Interface (API) calls in the smartphone. This classification is developed using static analysis in a controlled lab environment and the Drebin dataset is used as the training dataset. 800 apps from the Google Play Store have been chosen randomly to test the proposed classification. As a result, 16 permissions and 31 API calls that are most related with mobile botnet have been extracted using feature selection and later classified and tested using machine learning algorithms. The experimental result shows that the Random Forest Algorithm has achieved the highest detection accuracy of 99.4% with the lowest false positive rate of 16.1% as compared to other machine learning algorithms. This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.
dc.identifier.epage126
dc.identifier.scopusWOS:000427348400021
dc.identifier.spage121
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12131
dc.languageEnglish
dc.language.isoen_US
dc.publisherIEEEen_US
dc.relation.ispartof2017 Seventh International Conference On Emerging Security Technologies (Est)
dc.sourceWeb Of Science (ISI)
dc.subjectandroid botneten_US
dc.subjectmachine learningen_US
dc.subjectmobile botnet classificationen_US
dc.subjectstatic analysisen_US
dc.subjectrandom forest algorithmen_US
dc.titleA New Mobile Botnet Classification based on Permission and API Calls
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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