2022
DOI: 10.3390/s22207928
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Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review

Abstract: Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective. Different malware and their variants are emerging every day, making it a huge challenge to come up with detection and preventive methodologies and to… Show more

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Cited by 5 publications
(4 citation statements)
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“…Android malware analysis. There have been several systematic literature reviews about different aspects of Android malware detection techniques [16], [100], [17], [112], [112], [113], [114], [115], [116], [117], [118]. For instance, Senanayake et al [16] wrote about ML techniques in general, whereas Liue et al [100] and Qiu et al [112] focused on comparing deep learning detection techniques.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Android malware analysis. There have been several systematic literature reviews about different aspects of Android malware detection techniques [16], [100], [17], [112], [112], [113], [114], [115], [116], [117], [118]. For instance, Senanayake et al [16] wrote about ML techniques in general, whereas Liue et al [100] and Qiu et al [112] focused on comparing deep learning detection techniques.…”
Section: Related Workmentioning
confidence: 99%
“…Pan et al [17] set their research focus on static-analysis techniques. Ehsan et al [114] also analysed static-analysis techniques but set their main objective on studying methods that use app permissions for detection.…”
Section: Related Workmentioning
confidence: 99%
“…A systematic literature review (SLR) exploring static scanning techniques for Android OS malware detection (Ehsan et al, 2022). Their findings point out the superiority of neural models over non-neural networks in terms of performance.…”
Section: Related Workmentioning
confidence: 99%
“…The potential of these techniques is to learn from the data that are provided and, as a result, extract valuable insights and correctly predict cases in the future. By enabling automated and intelligent analysis of intricate patterns, features, and behaviors within data, ML and DL play a significant role in malware detection [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. These approaches enable security systems to accurately detect and classify malware with high accuracy, even as malware evolves and becomes more sophisticated.…”
Section: Introductionmentioning
confidence: 99%