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Adversarial Patterns: Building Robust Android Malware Classifiers

Adversarial Patterns: Building Robust Android Malware Classifiers

4 March 2022
Dipkamal Bhusal
Nidhi Rastogi
    AAML
ArXivPDFHTML

Papers citing "Adversarial Patterns: Building Robust Android Malware Classifiers"

6 / 6 papers shown
Title
Machine Learning Security in Industry: A Quantitative Survey
Machine Learning Security in Industry: A Quantitative Survey
Kathrin Grosse
L. Bieringer
Tarek R. Besold
Battista Biggio
Katharina Krombholz
21
31
0
11 Jul 2022
Problem-Space Evasion Attacks in the Android OS: a Survey
Problem-Space Evasion Attacks in the Android OS: a Survey
Harel Berger
Chen Hajaj
A. Dvir
11
2
0
29 May 2022
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
222
1,818
0
03 Feb 2017
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,813
0
08 Jul 2016
A Random Forest Guided Tour
A Random Forest Guided Tour
Gérard Biau
Erwan Scornet
AI4TS
137
2,701
0
18 Nov 2015
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
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