Publication:
Android Mobile Malware Classification using Tokenization Approach based on System Call Sequence

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dc.ConferencecodeInt Assoc Engineers, IAENG, Soc Artificial Intelligence, IAENG, Soc Bioinformat, IAENG, Soc Comp Sci, IAENG, Soc Data Min, IAENG, Soc Elect Engn, IAENG, Soc HIV AIDS, IAENG, Soc Imag Engn, IAENG, Soc Ind Engn, IAENG, Soc Informat Syst Engn, IAENG, Soc internet Comp & Web Serv, IAENG, Soc Mech Engn, IAENG, Soc Operat Res, IAENG, Soc Sci Comp, IAENG, Soc Software Engn, IAENG, Soc Wireless Networks
dc.ConferencedateOCT 25-27, 2017
dc.ConferencelocationSan Francisco, CA
dc.ConferencenameWorld Congress on Engineering and Computer Science, WCES 2017
dc.contributor.authorAhmad, INen_US
dc.contributor.authorRidzuan, Fen_US
dc.contributor.authorSaudi, MMen_US
dc.contributor.authorPitchay, SAen_US
dc.contributor.authorBasir, Nen_US
dc.contributor.authorNabila, NFen_US
dc.date.accessioned2024-05-29T03:27:22Z
dc.date.available2024-05-29T03:27:22Z
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. Naive 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.
dc.identifier.epage90
dc.identifier.issn2078-0958
dc.identifier.scopusWOS:000418106200018
dc.identifier.spage85
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12208
dc.languageEnglish
dc.language.isoen_US
dc.publisherInt Assoc Engineers-Iaengen_US
dc.relation.ispartofWorld Congress On Engineering And Computer Science, Wcecs 2017, Vol I
dc.sourceWeb Of Science (ISI)
dc.subjectAndroid mobile malwareen_US
dc.subjectmobile malware classificationen_US
dc.subjectsystem call sequenceen_US
dc.subjecttokenizationen_US
dc.titleAndroid Mobile Malware Classification using Tokenization Approach based on System Call Sequence
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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