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Do Gradient-based Explanations Tell Anything About Adversarial
  Robustness to Android Malware?

Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?

4 May 2020
Marco Melis
Michele Scalas
Ambra Demontis
Davide Maiorca
Battista Biggio
Giorgio Giacinto
Fabio Roli
    AAML
    FAtt
ArXivPDFHTML

Papers citing "Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?"

4 / 4 papers shown
Title
On building machine learning pipelines for Android malware detection: a
  procedural survey of practices, challenges and opportunities
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities
Masoud Mehrabi Koushki
I. Abualhaol
Anandharaju Durai Raju
Yang Zhou
Ronnie Salvador Giagone
Huang Shengqiang
18
11
0
12 Jun 2023
Explainable Artificial Intelligence and Cybersecurity: A Systematic
  Literature Review
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review
C. Mendes
T. N. Rios
22
7
0
27 Feb 2023
A Longitudinal Study of Cryptographic API: a Decade of Android Malware
A Longitudinal Study of Cryptographic API: a Decade of Android Malware
Adam Janovsky
Davide Maiorca
Dominik Macko
Vashek Matyás
Giorgio Giacinto
14
1
0
11 May 2022
Developing Future Human-Centered Smart Cities: Critical Analysis of
  Smart City Security, Interpretability, and Ethical Challenges
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
Kashif Ahmad
Majdi Maabreh
M. Ghaly
Khalil Khan
Junaid Qadir
Ala I. Al-Fuqaha
27
142
0
14 Dec 2020
1