1 option
Decision and Game Theory for Security : 12th International Conference, GameSec 2021, Virtual Event, October 25-27, 2021, Proceedings / edited by Branislav Bošanský, Cleotilde Gonzalez, Stefan Rass, Arunesh Sinha.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Security and cryptology ; SL 4, 13061
- Security and Cryptology ; 13061
- Language:
- English
- Subjects (All):
- Data protection.
- Artificial intelligence.
- Application software.
- Computer engineering.
- Computer networks.
- Computers.
- Data and Information Security.
- Artificial Intelligence.
- Computer and Information Systems Applications.
- Computer Engineering and Networks.
- Computing Milieux.
- Local Subjects:
- Data and Information Security.
- Artificial Intelligence.
- Computer and Information Systems Applications.
- Computer Engineering and Networks.
- Computing Milieux.
- Physical Description:
- 1 online resource (XIII, 379 pages) : 88 illustrations, 76 illustrations in color.
- Edition:
- 1st ed. 2021.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2021.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the 12th International Conference on Decision and Game Theory for Security, GameSec 2021,held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 20 full papers presented were carefully reviewed and selected from 37 submissions. The papers focus on Theoretical Foundations in Equilibrium Computation; Machine Learning and Game Theory; Ransomware; Cyber-Physical Systems Security; Innovations in Attacks and Defenses.
- Contents:
- Computing Nash Equilibria in Multiplayer DAG-Structured Stochastic Games with Persistent Imperfect Information
- Two Algorithms for Computing Exact and Approximate Nash Equilibria in Bimatrix Games
- Separable Network Games With Compact Strategy Sets
- Countering Attacker Data Manipulation in Security Games
- Scalable Optimal Classifiers for Adversarial Settings under Uncertainty
- Learning Generative Deception Strategies in Combinatorial Masking Games
- Network Games with Strategic Machine Learning
- No Time To Lie: Bounds on the Learning Rate of a Defender for Inferring Attacker Target Preferences
- When Should You Defend Your Classifier?
- A Game-theoretical Analysis of Countermeasures against Adversarial Examples
- A Mechanism Design Approach to Solve Ransomware Dilemmas
- Winning the Ransomware Lottery
- Combating Ransomware in Internet of Things: A Games-in-Games Approach for Cross-Layer Cyber Defense and Security Investment.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-90370-1
- 9783030903701
- Access Restriction:
- Restricted for use by site license.
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