The rise of obfuscated Android malware and impacts on detection methods.

Android malware Android security Evasion techniques Machine learning Obfuscation techniques

Journal

PeerJ. Computer science
ISSN: 2376-5992
Titre abrégé: PeerJ Comput Sci
Pays: United States
ID NLM: 101660598

Informations de publication

Date de publication:
2022
Historique:
received: 14 09 2021
accepted: 08 02 2022
entrez: 2 5 2022
pubmed: 3 5 2022
medline: 3 5 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 35494876
doi: 10.7717/peerj-cs.907
pii: cs-907
pmc: PMC9044361
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e907

Informations de copyright

© 2022 Elsersy et al.

Déclaration de conflit d'intérêts

The authors declare that they have no competing interests.

Références

PLoS One. 2016 Sep 09;11(9):e0162627
pubmed: 27611312
PeerJ Comput Sci. 2021 Jun 11;7:e522
pubmed: 34825052

Auteurs

Wael F Elsersy (WF)

Department of Computer System and Technology/Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.

Ali Feizollah (A)

Department of Computer System and Technology/Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.

Nor Badrul Anuar (NB)

Department of Computer System and Technology/Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.

Classifications MeSH