Please use this identifier to cite or link to this item: http://ir.futminna.edu.ng:8080/jspui/handle/123456789/18069
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dc.contributor.authorOjeniyi, Joseph Adebayo-
dc.contributor.authorHussaini, Y.-
dc.contributor.authorUmar, M.-
dc.contributor.authorAbdullazeez, A.-
dc.contributor.authorOladipupo, S.F.-
dc.date.accessioned2023-02-14T08:07:08Z-
dc.date.available2023-02-14T08:07:08Z-
dc.date.issued2022-12-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/18069-
dc.description.abstractAndroid malware is growing at alarming rate and spreading rapidly despite on-going mitigating efforts. This brings a necessity to find more effective solutions to detect those malwares and prevent users from any malicious threats. This study uses the PRISMA statement as a reference so to be transparent. This paper uses a SLR to identify where recent studies in Android malware detection have been focused on and offers a broad perspective relating to types of analysis and the dataset sources used in the research area within the range of 2015 to 2020. A total of 58 selected papers met the inclusion criteria based on title of articles, exclusion criteria, reading abstract and content of the selected 58 papers. Different data are extracted from these articles and recorded in an excel sheet for further analysis. Most of the paper discussed about the use of systematic analysis approach to analyze malware using Debrin dataset, Google Play dataset and Virus Share dataset samples. The systematic review carried out would provide information to all researchers and further inform the requirements for future development of enhanced malware analysis and detection methods.en_US
dc.language.isoenen_US
dc.publisherAccraBeskspoke Innovations Conferenceen_US
dc.subjectfuture development of enhanced malware analysis and detection methods.en_US
dc.titleAndroid malware detection: A systematic Literature reviewen_US
dc.typeArticleen_US
Appears in Collections:Cyber Security Science

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