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The Rise of Obfuscated Android Malware and Impacts on Detection Methods

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Date 2022 May 2
PMID 35494876
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Abstract

The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques. The study concludes the recent Android malware detection-related issues and lessons learned which require researchers' attention in the future.

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References
1.
Afifi F, Anuar N, Shamshirband S, Choo K . DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware. PLoS One. 2016; 11(9):e0162627. PMC: 5017788. DOI: 10.1371/journal.pone.0162627. View

2.
Jusoh R, Firdaus A, Anwar S, Osman M, Darmawan M, Ab Razak M . Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation). PeerJ Comput Sci. 2021; 7:e522. PMC: 8594292. DOI: 10.7717/peerj-cs.522. View