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ABC: Android botnet classification using feature selection and classification algorithms

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dc.FundingDetailsUniversiti Tun Hussein Onn Malaysia Ministry of Higher Education, Malaysia,�MOHE: PPP/USG-0116/FST/30/13216,�USIM/FRGS/FST/32/50114 Universiti Sains Islam Malaysia
dc.FundingDetailsThe authors would like to express their gratitude to Universiti Sains Islam Malaysia, Islamic Science Institute (ISI) and Universiti Tun Hussein Onn, Malaysia (UTHM) for the support and facilities provided in conducting this research. This research paper is supported by Ministry of Higher Education grants [USIM/FRGS/FST/32/50114] and [PPP/USG-0116/FST/30/13216].
dc.citedby3
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.affiliationsUniversiti Tun Hussein Onn Malaysia (UTHM)
dc.contributor.affiliationsUniversity of Malaya (UM)
dc.contributor.authorAbdullah Z.en_US
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorAnuar N.B.en_US
dc.date.accessioned2024-05-28T08:24:38Z
dc.date.available2024-05-28T08:24:38Z
dc.date.issued2017
dc.description.abstractSmartphones have become an important part of human lives, and this led to an increase number of smartphone users. However, this also attracts hackers to develop malicious applications especially Android botnet to steal the private information and causing financial losses. Due to the fast modifications in the technologies used by malicious application (app) developers, there is an urgent need for more advanced techniques for Android botnet detection. In this paper, a new approach for Android botnet classification based on features selection and classification algorithms is proposed. The proposed approach uses the permissions requested in the Android app as features, to differentiate between the Android botnet apps and benign apps. The Information Gain algorithm is used to select the most significant permissions, then the classification algorithms Na�ve Bayes, Random Forest and J48 used to classify the Android apps as botnet or benign apps. The experimental results show that Random Forest Algorithm achieved the highest detection accuracy of 94.6% with lowest false positive rate of 0.099. � 2017 American Scientific Publishers All rights reserved.
dc.description.natureFinalen_US
dc.identifier.doi10.1166/asl.2017.8994
dc.identifier.epage4720
dc.identifier.issn19366612
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85023748379
dc.identifier.spage4717
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85023748379&doi=10.1166%2fasl.2017.8994&partnerID=40&md5=78ffa76fee23ea537df20173d8de7f4e
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8532
dc.identifier.volume23
dc.languageEnglish
dc.language.isoen_US
dc.publisherAmerican Scientific Publishersen_US
dc.relation.ispartofAdvanced Science Letters
dc.sourceScopus
dc.subjectAndroid botneten_US
dc.subjectClassificationen_US
dc.subjectInformation gainen_US
dc.titleABC: Android botnet classification using feature selection and classification algorithms
dc.typeArticleen_US
dspace.entity.typePublication

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