Please use this identifier to cite or link to this item: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/18704
Title: Systematic Literature Review on Android Malware Detection
Authors: Anyaora, P.C
Adebayo, O.S
Ismaila, I
Ojeniyi, J.A
Olalere, M
Keywords: Android malware detection, dynamic analysis, static analysis, machine learning algorithm.
Issue Date: 2023
Publisher: 4th International Engineering Conference (IEC 2022)
Abstract: Users 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.
URI: http://repository.futminna.edu.ng:8080/jspui/handle/123456789/18704
Appears in Collections:Cyber Security Science

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