| TÃtulo : |
11th International Conference, GameSec 2020, College Park, MD, USA, October 28–30, 2020, Proceedings |
| Tipo de documento: |
documento electrónico |
| Autores: |
Zhu, Quanyan, ; Baras, John S., ; Poovendran, Radha, ; Chen, Juntao, |
| Mención de edición: |
1 ed. |
| Editorial: |
[s.l.] : Springer |
| Fecha de publicación: |
2020 |
| Número de páginas: |
XI, 518 p. 131 ilustraciones, 111 ilustraciones en color. |
| ISBN/ISSN/DL: |
978-3-030-64793-3 |
| Nota general: |
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos. |
| Palabras clave: |
Protección de datos Inteligencia artificial CriptografÃa Cifrado de datos (Informática) Red informática Procesamiento de imágenes Visión por computador IngenierÃa Informática Red de computadoras Seguridad de datos e información CriptologÃa Seguridad móvil y de red Imágenes por computadora visión reconocimiento de patrones y gráficos IngenierÃa Informática y Redes |
| Clasificación: |
005.8 Ciencia de los computadores (Programación, programas de sistemas) |
| Resumen: |
Este libro constituye las actas arbitradas de la 11.ª Conferencia Internacional sobre Decisión y TeorÃa de Juegos para la Seguridad, GameSec 2020, celebrada en College Park, MD, EE. UU., en octubre de 2020. Debido a la pandemia de COVID-19, la conferencia se llevó a cabo de forma virtual. Los 21 artÃculos completos presentados junto con 2 artÃculos breves fueron cuidadosamente revisados ​​y seleccionados entre 29 presentaciones. Los artÃculos se centran en el aprendizaje automático y la seguridad; engaño cibernético; seguridad de sistemas ciberfÃsicos; seguridad de los sistemas de red; fundamentos teóricos de los juegos de seguridad; temas emergentes. |
| Nota de contenido: |
Machine Learning and Security -- Distributed Generative Adversarial Networks for Anomaly Detection -- Learning and Planning in the Feature Deception Problem -- A Realistic Approach for Network Traffic Obfuscation using Adversarial Machine Learning -- Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense -- Lie Another Day: Demonstrating Bias in a Multi-Round Cyber Deception Game of Questionable Veracity -- Cyber Deception -- Exploiting Bounded Rationality in Risk-based Cyber Camouflage Games -- Farsighted Risk Mitigation of Lateral Movement Using Dynamic Cognitive Honeypots -- Harnessing the Power of Deception in Attack Graph-Based Security Games -- Decoy Allocation Games on Graphs with Temporal Logic Objectives -- Popular Imperceptibility Measures in Visual Adversarial Attacks are Far from Human Perception -- Cyber-Physical System Security -- Secure Discrete-Time Linear-Quadratic Mean-Field Games -- Detection of Dynamically Changing Leaders in Complex Swarms from ObservedDynamic Data -- Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments -- Security of Network Systems -- Blocking Adversarial Influence in Social Networks -- Normalizing Flow Policies for Multi-agent Systems -- A Game Theoretic Framework for Software Diversity for Network Security -- Partially Observable Stochastic Games for Cyber Deception against Network Epidemic -- Combating Online Counterfeits: A Game-Theoretic Analysis of Cyber Supply Chain Ecosystem -- Theoretic Foundations of Security Games -- On the Characterization of Saddle Point Equilibrium for Security Games with Additive Utility -- MASAGE: Model-Agnostic Sequential and Adaptive Game Estimation -- Using One-Sided Partially Observable Stochastic Games for Solving Zero-Sum Security Games with Sequential Attacks -- A Data-Driven Distributionally Robust Game using Wasserstein Distance -- Security Games over Lexicographic Orders -- Game Theory on Attack Graph for Cyber Deception -- Attacking Machine Learning Models for Social Good -- A Review of Multi Agent Perimeter Defense Games -- Hardware Security and Trust: A New Battlefield of Information -- A Data Mining Friendly Anonymization Scheme for System Logs using Distance Mapping -- Security Games with Insider Threats -- Securing Next-Generation Wireless Networks: Challenges and Opportunities. . |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
11th International Conference, GameSec 2020, College Park, MD, USA, October 28–30, 2020, Proceedings [documento electrónico] / Zhu, Quanyan, ; Baras, John S., ; Poovendran, Radha, ; Chen, Juntao, . - 1 ed. . - [s.l.] : Springer, 2020 . - XI, 518 p. 131 ilustraciones, 111 ilustraciones en color. ISBN : 978-3-030-64793-3 Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
| Palabras clave: |
Protección de datos Inteligencia artificial CriptografÃa Cifrado de datos (Informática) Red informática Procesamiento de imágenes Visión por computador IngenierÃa Informática Red de computadoras Seguridad de datos e información CriptologÃa Seguridad móvil y de red Imágenes por computadora visión reconocimiento de patrones y gráficos IngenierÃa Informática y Redes |
| Clasificación: |
005.8 Ciencia de los computadores (Programación, programas de sistemas) |
| Resumen: |
Este libro constituye las actas arbitradas de la 11.ª Conferencia Internacional sobre Decisión y TeorÃa de Juegos para la Seguridad, GameSec 2020, celebrada en College Park, MD, EE. UU., en octubre de 2020. Debido a la pandemia de COVID-19, la conferencia se llevó a cabo de forma virtual. Los 21 artÃculos completos presentados junto con 2 artÃculos breves fueron cuidadosamente revisados ​​y seleccionados entre 29 presentaciones. Los artÃculos se centran en el aprendizaje automático y la seguridad; engaño cibernético; seguridad de sistemas ciberfÃsicos; seguridad de los sistemas de red; fundamentos teóricos de los juegos de seguridad; temas emergentes. |
| Nota de contenido: |
Machine Learning and Security -- Distributed Generative Adversarial Networks for Anomaly Detection -- Learning and Planning in the Feature Deception Problem -- A Realistic Approach for Network Traffic Obfuscation using Adversarial Machine Learning -- Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense -- Lie Another Day: Demonstrating Bias in a Multi-Round Cyber Deception Game of Questionable Veracity -- Cyber Deception -- Exploiting Bounded Rationality in Risk-based Cyber Camouflage Games -- Farsighted Risk Mitigation of Lateral Movement Using Dynamic Cognitive Honeypots -- Harnessing the Power of Deception in Attack Graph-Based Security Games -- Decoy Allocation Games on Graphs with Temporal Logic Objectives -- Popular Imperceptibility Measures in Visual Adversarial Attacks are Far from Human Perception -- Cyber-Physical System Security -- Secure Discrete-Time Linear-Quadratic Mean-Field Games -- Detection of Dynamically Changing Leaders in Complex Swarms from ObservedDynamic Data -- Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments -- Security of Network Systems -- Blocking Adversarial Influence in Social Networks -- Normalizing Flow Policies for Multi-agent Systems -- A Game Theoretic Framework for Software Diversity for Network Security -- Partially Observable Stochastic Games for Cyber Deception against Network Epidemic -- Combating Online Counterfeits: A Game-Theoretic Analysis of Cyber Supply Chain Ecosystem -- Theoretic Foundations of Security Games -- On the Characterization of Saddle Point Equilibrium for Security Games with Additive Utility -- MASAGE: Model-Agnostic Sequential and Adaptive Game Estimation -- Using One-Sided Partially Observable Stochastic Games for Solving Zero-Sum Security Games with Sequential Attacks -- A Data-Driven Distributionally Robust Game using Wasserstein Distance -- Security Games over Lexicographic Orders -- Game Theory on Attack Graph for Cyber Deception -- Attacking Machine Learning Models for Social Good -- A Review of Multi Agent Perimeter Defense Games -- Hardware Security and Trust: A New Battlefield of Information -- A Data Mining Friendly Anonymization Scheme for System Logs using Distance Mapping -- Security Games with Insider Threats -- Securing Next-Generation Wireless Networks: Challenges and Opportunities. . |
| Enlace de acceso : |
https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...] |
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