Foundations and Practice of Security (eBook, PDF)
14th International Symposium, FPS 2021, Paris, France, December 7-10, 2021, Revised Selected Papers
Redaktion: Aïmeur, Esma; Garcia-Alfaro, Joaquin; Dupont, Benoît; Yaich, Reda; Laurent, Maryline
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This book constitutes the revised selected papers of the 14th International Symposium on Foundations and Practice of Security, FPS 2021, held in Paris, France, in December 2021.The 18 full papers and 9 short paper presented in this book were carefully reviewed and selected from 62 submissions. They cover a range of topics such as Analysis and Detection; Prevention and Efficiency; and Privacy by Design.Chapters "A Quantile-based Watermarking Approach for Distortion Minimization", "Choosing Wordlists for Password Guessing: An Adaptive Multi-Armed Bandit Approach" and "A Comparative Analysis of M...
This book constitutes the revised selected papers of the 14th International Symposium on Foundations and Practice of Security, FPS 2021, held in Paris, France, in December 2021.
The 18 full papers and 9 short paper presented in this book were carefully reviewed and selected from 62 submissions. They cover a range of topics such as Analysis and Detection; Prevention and Efficiency; and Privacy by Design.
Chapters "A Quantile-based Watermarking Approach for Distortion Minimization", "Choosing Wordlists for Password Guessing: An Adaptive Multi-Armed Bandit Approach" and "A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
The 18 full papers and 9 short paper presented in this book were carefully reviewed and selected from 62 submissions. They cover a range of topics such as Analysis and Detection; Prevention and Efficiency; and Privacy by Design.
Chapters "A Quantile-based Watermarking Approach for Distortion Minimization", "Choosing Wordlists for Password Guessing: An Adaptive Multi-Armed Bandit Approach" and "A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
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