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The Evolution of Android Malware and Android Analysis Techniques

Authors:

Nor Badrul Anuar,

Lorenzo CavallaroAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 49, Issue 4

Article No.: 76, Pages 1 - 41

https://doi.org/10.1145/3017427

Published: 13 January 2017 Publication History

Abstract

With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. This article categorizes systems by methodology and date to evaluate progression and weaknesses. This article also discusses evaluations of industry solutions, malware statistics, and malware evasion techniques and concludes by supporting future research paths.

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Arrowsmith JSusnjak TJang-Jaccard J(2025)Multimodal Deep Learning for Android Malware ClassificationMachine Learning and Knowledge Extraction10.3390/make70100237:1(23)Online publication date: 28-Feb-2025
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cover image ACM Computing Surveys

ACM Computing Surveys Volume 49, Issue 4

December 2017

666 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3022634

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Publication History

Published: 13 January 2017

Accepted: 01 November 2016

Revised: 01 September 2016

Received: 01 May 2015

Published in CSUR Volume 49, Issue 4

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https://doi.org/10.3390/make7010023
Sharma TRattan D(2025)Characterization of Android Malwares and their familiesACM Computing Surveys10.1145/370850057:5(1-31)Online publication date: 6-Jan-2025
https://dl.acm.org/doi/10.1145/3708500
Gupta RSharma KGarg R(2024)Innovative Approach to Android Malware Detection: Prioritizing Critical Features Using Rough Set TheoryElectronics10.3390/electronics1303048213:3(482)Online publication date: 23-Jan-2024
https://doi.org/10.3390/electronics13030482
Wajahat AHe JZhu NMahmood TNazir AUllah FQureshi SOsman M(2024)An effective deep learning scheme for android malware detection leveraging performance metrics and computational resourcesIntelligent Decision Technologies10.3233/IDT-23028418:1(33-55)Online publication date: 1-Jan-2024
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Gao CHuang GLi HWu BWu YYuan WRoychoudhury APaiva AAbreu RStorey M(2024)A Comprehensive Study of Learning-based Android Malware Detectors under Challenging EnvironmentsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623320(1-13)Online publication date: 20-May-2024
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