Publication:
Android mobile malware classification using tokenization approach based on system call sequence

Simple item page
dc.Conferencecode136514
dc.Conferencedate25 October 2017 through 27 October 2017
dc.Conferencename2017 World Congress on Engineering and Computer Science, WCECS 2017
dc.FundingDetailsMinistry of Higher Education, Malaysia: FRGS/FST/32/50114 Universiti Sains Islam Malaysia Universiti Sains Islam Malaysia
dc.FundingDetailsManuscript received July 16, 2017; revised August 8, 2017. This work was funded by Ministry of Higher Education (Malaysia), FRGS grant: [FRGS/FST/32/50114].
dc.FundingDetailsThe authors would like to express their gratitude to Ministry of Higher Education Malaysia and Universiti Sains Islam Malaysia (USIM) for the support and facilities provided.
dc.citedby4
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorAhmad I.N.en_US
dc.contributor.authorRidzuan F.en_US
dc.contributor.authorSaudi M.M.en_US
dc.contributor.authorPitchay S.A.en_US
dc.contributor.authorBasir N.en_US
dc.contributor.authorNabila N.F.en_US
dc.date.accessioned2024-05-29T01:54:42Z
dc.date.available2024-05-29T01:54:42Z
dc.date.issued2017
dc.description.abstractThe increasing number of smartphone over the last few years reflects an impressive growth in the number of advanced malicious applications targeting the smartphone users. Recently, Android has become the most popular operating system opted by users and the most targeted platform for smartphone malware attack. Besides, current mobile malware classification and detection approaches are relatively immature as the new advanced malware exploitation and threats are difficult to be detected. Therefore, an efficient approach is proposed to improve the performance of the mobile malware classification and detection. In this research, a new system call classification with call logs exploitation for mobile attacks has been developed using tokenization approach. The experiment was conducted using static and dynamic-based analysis approach in a controlled lab. System calls with call logs exploitation from 5560 Drebin samples were extracted and further examined. This research paper aims to find the best n value and classifier in classifying the dataset based on the new patterns produced. Na�ve Bayes classifier has successfully achieved accuracy of 99.86% which gives the best result among other classifiers. This new system call classification can be used as a guidance and reference for other researchers in the same field for security against mobile malware attacks targeted to call logs exploitation. � Copyright International Association of Engineers.
dc.description.natureFinalen_US
dc.editorAo S.I.Grundfest W.S.Douglas C.en_US
dc.identifier.epage90
dc.identifier.isbn9789880000000
dc.identifier.issn20780958
dc.identifier.scopus2-s2.0-85050079816
dc.identifier.spage85
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85050079816&partnerID=40&md5=b86fdf974a3174a5c037641f4a035b00
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/9518
dc.identifier.volume1
dc.languageEnglish
dc.language.isoen_US
dc.publisherNewswood Limiteden_US
dc.relation.ispartofLecture Notes in Engineering and Computer Science
dc.sourceScopus
dc.subjectAndroid mobile malwareen_US
dc.subjectMobile malware classificationen_US
dc.subjectSystem call sequenceen_US
dc.subjectTokenization.en_US
dc.subjectAndroid (operating system)en_US
dc.subjectClassification (of information)en_US
dc.subjectComputer crimeen_US
dc.subjectIntrusion detectionen_US
dc.subjectSmartphonesen_US
dc.subjectAnalysis approachen_US
dc.subjectBayes Classifieren_US
dc.subjectDetection approachen_US
dc.subjectMobile malwareen_US
dc.subjectResearch papersen_US
dc.subjectSmartphone malwareen_US
dc.subjectSystem-call sequenceen_US
dc.subjectTokenizationen_US
dc.subjectMalwareen_US
dc.titleAndroid mobile malware classification using tokenization approach based on system call sequence
dc.typeConference Paperen_US
dspace.entity.typePublication

Files

Collections

Scopus