Explainability versus Security: The Unintended Consequences of xAI in Cybersecurity
Author:
Affiliation:
1. ITTI Sp. z o.o., Poznań, Poland, Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
2. ITTI Sp. z o.o., Poznań, Poland, University of Warsaw, Warsaw, Poland
Funder
European Comission
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3665451.3665527
Reference29 articles.
1. Malik AL-Essa Giuseppina Andresini Annalisa Appice and Donato Malerba. 2022. XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity. 117--126. https://doi.org/10.1007/978-3-031-16564-1_12
2. Hubert Baniecki and Przemyslaw Biecek. 2023. Adversarial Attacks and Defenses in Explainable Artificial Intelligence: A Survey. (jun 2023). https://doi.org/2306.06123v2 arXiv:2306.06123
3. Gérard Biau. 2012. Analysis of a Random Forests Model. J. Mach. Learn. Res. 13, null (apr 2012), 1063--1095.
4. L. Breiman J. Friedman C.J. Stone and R.A. Olshen. 1984. Classification and Regression Trees. Taylor & Francis. https://books.google.pl/books?id=JwQx-WOmSyQC
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