SeqAdver: Automatic payload construction and injection in sequence-based Android adversarial attack
Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning ha...
Saved in:
Main Authors: | ZHANG, Fei, FENG, Ruitao, XIE, Xiaofei, LI, Xiaohong, SHI, Lianshuan |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/8707 https://ink.library.smu.edu.sg/context/sis_research/article/9710/viewcontent/SeqAdver_av.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Understanding Android app piggybacking: A systematic study of malicious code grafting
by: LI, Li, et al.
Published: (2017) -
Adversarial attacks and robustness for segment anything model
by: Liu, Shifei
Published: (2024) -
A software environment for confining malicious android applications via resource virtualization
by: Li, X., et al.
Published: (2014) -
SeqNinja : automatic payload re-construction and manipulation in sequence-based android adversarial attack
by: Ang, Hao Jie
Published: (2021) -
Amora: Black-box adversarial morphing attack
by: WANG, Run, et al.
Published: (2020)