Data-driven android malware intelligence : a survey
Version 3 2025-06-03, 03:41Version 3 2025-06-03, 03:41
Version 2 2024-06-06, 00:31Version 2 2024-06-06, 00:31
Version 1 2019-09-26, 10:54Version 1 2019-09-26, 10:54
conference contribution
posted on 2025-06-03, 03:41authored byJunyang 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