Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/18491
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dc.contributor.authorAnyaora, Peter C.-
dc.contributor.authorAdebayo, Olawale Surajudeen-
dc.contributor.authorIsmaila, Idris-
dc.contributor.authorOjeniyi, Joseph-
dc.contributor.authorOlalere, Morufu-
dc.date.accessioned2023-04-29T09:47:28Z-
dc.date.available2023-04-29T09:47:28Z-
dc.date.issued2023-03-22-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/18491-
dc.description.abstractUsers of Android-powered smartphones and tablets have multiplied dramatically. Thanks to Android third-party apps, the essential applications, such as banking and healthcare, are accessible on Android smartphones. There are new threats to be taken into account about harmful programs when these applications are utilized and embraced more broadly. This research performs a systematic literature review using the prima framework and Kitchenham statement to apply on android malware detection and analysis of different methodology of publishing research that have been used for android malware detection for the last past five years. Using the keyword” Android malware detection” the research had seen over 610 published articles on” Android Malware detection”. It was narrowed down to 142 published research papers due to it between the year 2018 to 2022 that was looked at, sixty-five articles (65) were finally selected for investigation after inclusion and exclusion. One of the research key findings is the performance of Machine Learning (ML) algorithms which were relatively higher than others.en_US
dc.description.sponsorshipFederal University of Technology Minnaen_US
dc.language.isoenen_US
dc.subjectAndroid malware detectionen_US
dc.subjectDynamic analysisen_US
dc.subjectstatic analysisen_US
dc.subjectMachine learning algorithmen_US
dc.titleSystematic Literature Review on Android Malware Detectionen_US
dc.typeArticleen_US
Appears in Collections:Cyber Security Science

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