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Data-driven android malware intelligence : a survey

Version 3 2025-06-03, 03:41
Version 2 2024-06-06, 00:31
Version 1 2019-09-26, 10:54
conference contribution
posted on 2025-06-03, 03:41 authored by Junyang Qiu, Surya Nepal, Wei LuoWei Luo, Lei Pan, Yonghang Tai, Jun Zhang, Yang Xiang
Android has dominated the smartphone market and become the most popular mobile operating system. This rapidly increasing market share of Android has contributed to the boom of Android malware in numbers and in varieties. There exist many techniques which are proposed to accurately detect malware, e.g., software engineering-based techniques and machine learning (ML)-based techniques. In this paper, our main contributions are threefold: We reviewed the existing analysis techniques for Android malware detection; We focused on the code analysis based detection techniques under the ML frameworks; We gave the future research challenges and directions about Android malware analysis.

History

Volume

11806

Pagination

1-18

Location

Xi'an, China

Start date

2019-09-19

End date

2019-09-21

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030306199

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Chen X, Huang X, Zhang J

Title of proceedings

ML4CS 2019 : Machine Learning for Cyber Security, Second International Conference, ML4CS 2019 Xi'an, China, September 19-21, 2019 Proceedings

Event

Machine Learning for Cyber Security/ Conference (2019 : 2nd: Xi'an, China)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Lecture Notes in Computer Science