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Efficient Query-Based Attack against ML-Based Android Malware Detection
  under Zero Knowledge Setting
v1v2 (latest)

Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting

5 September 2023
Ping He
Yifan Xia
Xuhong Zhang
Shouling Ji
    AAML
ArXiv (abs)PDFHTML

Papers citing "Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting"

19 / 19 papers shown
Title
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Ping He
Lorenzo Cavallaro
Shouling Ji
AAML
204
0
0
23 Jan 2025
"Real Attackers Don't Compute Gradients": Bridging the Gap Between
  Adversarial ML Research and Practice
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Giovanni Apruzzese
Hyrum S. Anderson
Savino Dambra
D. Freeman
Fabio Pierazzi
Kevin A. Roundy
AAML
101
81
0
29 Dec 2022
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision
  Exploit
RamBoAttack: A Robust Query Efficient Deep Neural Network Decision Exploit
Viet Vo
Ehsan Abbasnejad
Damith C. Ranasinghe
AAML
68
9
0
10 Dec 2021
A Large-scale Temporal Measurement of Android Malicious Apps:
  Persistence, Migration, and Lessons Learned
A Large-scale Temporal Measurement of Android Malicious Apps: Persistence, Migration, and Lessons Learned
Yun Shen
Pierre-Antoine Vervier
Gianluca Stringhini
54
7
0
10 Aug 2021
Understanding Worldwide Private Information Collection on Android
Understanding Worldwide Private Information Collection on Android
Yun Shen
Pierre-Antoine Vervier
Gianluca Stringhini
PILM
40
14
0
25 Feb 2021
Transcending Transcend: Revisiting Malware Classification in the
  Presence of Concept Drift
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift
Federico Barbero
Feargus Pendlebury
Fabio Pierazzi
Lorenzo Cavallaro
70
74
0
08 Oct 2020
SoK: Certified Robustness for Deep Neural Networks
SoK: Certified Robustness for Deep Neural Networks
Linyi Li
Tao Xie
Yue Liu
AAML
123
131
0
09 Sep 2020
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware
  Detection
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
Deqiang Li
Qianmu Li
AAML
66
126
0
30 Jun 2020
Why an Android App is Classified as Malware? Towards Malware
  Classification Interpretation
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
Bozhi Wu
Sen Chen
Cuiyun Gao
Lingling Fan
Yang Liu
W. Wen
Michael R. Lyu
86
58
0
24 Apr 2020
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack
Jianbo Chen
Michael I. Jordan
Martin J. Wainwright
AAML
107
670
0
03 Apr 2019
On Evaluating Adversarial Robustness
On Evaluating Adversarial Robustness
Nicholas Carlini
Anish Athalye
Nicolas Papernot
Wieland Brendel
Jonas Rauber
Dimitris Tsipras
Ian Goodfellow
Aleksander Madry
Alexey Kurakin
ELMAAML
117
905
0
18 Feb 2019
TextBugger: Generating Adversarial Text Against Real-world Applications
TextBugger: Generating Adversarial Text Against Real-world Applications
Jinfeng Li
S. Ji
Tianyu Du
Bo Li
Ting Wang
SILMAAML
216
747
0
13 Dec 2018
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning
  Detection
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Xiao Chen
Chaoran Li
Derui Wang
S. Wen
Jun Zhang
Surya Nepal
Yang Xiang
K. Ren
AAML
71
246
0
10 Aug 2018
TESSERACT: Eliminating Experimental Bias in Malware Classification
  across Space and Time
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Feargus Pendlebury
Fabio Pierazzi
Roberto Jordaney
Johannes Kinder
Lorenzo Cavallaro
88
359
0
20 Jul 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Battista Biggio
Fabio Roli
AAML
135
1,409
0
08 Dec 2017
Yes, Machine Learning Can Be More Secure! A Case Study on Android
  Malware Detection
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Ambra Demontis
Marco Melis
Battista Biggio
Davide Maiorca
Dan Arp
Konrad Rieck
Igino Corona
Giorgio Giacinto
Fabio Roli
AAML
66
284
0
28 Apr 2017
Towards Evaluating the Robustness of Neural Networks
Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini
D. Wagner
OODAAML
284
8,593
0
16 Aug 2016
Practical Black-Box Attacks against Machine Learning
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot
Patrick McDaniel
Ian Goodfellow
S. Jha
Z. Berkay Celik
A. Swami
MLAUAAML
85
3,687
0
08 Feb 2016
Intriguing properties of neural networks
Intriguing properties of neural networks
Christian Szegedy
Wojciech Zaremba
Ilya Sutskever
Joan Bruna
D. Erhan
Ian Goodfellow
Rob Fergus
AAML
297
14,978
1
21 Dec 2013
1