Publication:
Android Ransomware Detection Based on Dynamic Obtained Features

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dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Tun Hussein Onn Malaysia (UTHM)
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorAbdullah Z.en_US
dc.contributor.authorMuhadi F.W.en_US
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorHamid I.R.A.en_US
dc.contributor.authorFoozy C.F.M.en_US
dc.date.accessioned2024-05-29T02:03:16Z
dc.date.available2024-05-29T02:03:16Z
dc.date.issued2020
dc.description.abstractAlong with the rapid development of new science and technology, smartphone functionality has become more attractive. Smartphones not only bring convenience to the public but also the security risks at the same time through the installation of malicious applications. Among these, Android ransomware is gaining momentum and there is a need for effective defense as it is very important to ensure the security of smartphone user. There are various analysis techniques used to detect instances of Android ransomware. In this paper, we proposed the Android ransomware detection using dynamic analysis technique. Two dataset were used which is ransomware and benign dataset. The proposed approach used the system calls as features which obtained from dynamic analysis. The classification algorithms Random Forest, J48, and Naïve Bayes were used to classify the instances based on the proposed features. The experimental results showed that the Random Forest Algorithm achieved the highest detection accuracy of 98.31% with lowest false positive rate of 0.016.en_US
dc.identifier.doi10.1007/978-3-030-36056-6_12
dc.identifier.epage129
dc.identifier.isbn9.78E+12
dc.identifier.issn21945357
dc.identifier.scopus2-s2.0-85078476686
dc.identifier.spage121
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85078476686&doi=10.1007%2f978-3-030-36056-6_12&partnerID=40&md5=b55f40cb0d60a104d6f16bf34b153305
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10250
dc.identifier.volume978 AISC
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.relation.ispartofAdvances in Intelligent Systems and Computingen_US
dc.sourceScopus
dc.subjectAndroiden_US
dc.subjectClassificationen_US
dc.subjectDynamic analysisen_US
dc.subjectMachine learningen_US
dc.subjectRansomwareen_US
dc.titleAndroid Ransomware Detection Based on Dynamic Obtained Featuresen_US
dspace.entity.typePublication

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