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The rise of obfuscated Android malware and impacts on detection methods

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 develop...

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Detalles Bibliográficos
Autores principales: Elsersy, Wael F., Feizollah, Ali, Anuar, Nor Badrul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044361/
https://www.ncbi.nlm.nih.gov/pubmed/35494876
http://dx.doi.org/10.7717/peerj-cs.907
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author Elsersy, Wael F.
Feizollah, Ali
Anuar, Nor Badrul
author_facet Elsersy, Wael F.
Feizollah, Ali
Anuar, Nor Badrul
author_sort Elsersy, Wael F.
collection PubMed
description 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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spelling pubmed-90443612022-04-28 The rise of obfuscated Android malware and impacts on detection methods Elsersy, Wael F. Feizollah, Ali Anuar, Nor Badrul PeerJ Comput Sci Data Mining and Machine Learning 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. PeerJ Inc. 2022-03-09 /pmc/articles/PMC9044361/ /pubmed/35494876 http://dx.doi.org/10.7717/peerj-cs.907 Text en © 2022 Elsersy et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Mining and Machine Learning
Elsersy, Wael F.
Feizollah, Ali
Anuar, Nor Badrul
The rise of obfuscated Android malware and impacts on detection methods
title The rise of obfuscated Android malware and impacts on detection methods
title_full The rise of obfuscated Android malware and impacts on detection methods
title_fullStr The rise of obfuscated Android malware and impacts on detection methods
title_full_unstemmed The rise of obfuscated Android malware and impacts on detection methods
title_short The rise of obfuscated Android malware and impacts on detection methods
title_sort rise of obfuscated android malware and impacts on detection methods
topic Data Mining and Machine Learning
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044361/
https://www.ncbi.nlm.nih.gov/pubmed/35494876
http://dx.doi.org/10.7717/peerj-cs.907
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