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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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Cited By

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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Wang JWang HChristakis MPradel M(2024)NativeSummary: Summarizing Native Binary Code for Inter-language Static Analysis of Android AppsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680335(971-982)Online publication date: 11-Sep-2024
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Index Terms

The Evolution of Android Malware and Android Analysis Techniques
1. Security and privacy
  1. Systems security
    1. Operating systems security
      1. Mobile platform security

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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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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
https://dl.acm.org/doi/10.3233/IDT-230284
Wang JWang HChristakis MPradel M(2024)NativeSummary: Summarizing Native Binary Code for Inter-language Static Analysis of Android AppsProceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3650212.3680335(971-982)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3650212.3680335
Ruggia ANisi DDambra SMerlo ABalzarotti DAonzo SQuek TGao DZhou JCardenas A(2024)Unmasking the Veiled: A Comprehensive Analysis of Android Evasive MalwareProceedings of the 19th ACM Asia Conference on Computer and Communications Security10.1145/3634737.3637658(383-398)Online publication date: 1-Jul-2024
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Dickey KHwang DKim D(2024)Analyzing Various Machine Learning Approaches for Detecting Android MalwareSoutheastCon 202410.1109/SoutheastCon52093.2024.10500178(1288-1293)Online publication date: 15-Mar-2024
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Tippayasem NPiromsopa K(2024)Classification of Android Malware from Binary Code Using Ensemble Method with Recursive Feature Elimination2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE)10.1109/JCSSE61278.2024.10613651(174-178)Online publication date: 19-Jun-2024
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Al-Kahla WTaqieddin EShatnawi AAl-Ouran R(2024)Malware Detection and Classification in Android Application Using Simhash-Based Feature Extraction and Machine LearningIEEE Access10.1109/ACCESS.2024.350127712(174255-174273)Online publication date: 2024
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