Equilibria for Networks with Malicious Users 1st edition by George Karakostas, Anastasios ViglasI SBN 3540206958 9783540206958

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Product details:

ISBN 10:       3540206958
ISBN 13: 978-3540206958
Author:         George Karakostas, Anastasios ViglasI
 

Equilibria for Networks with Malicious Users 1st  Table of contents:

1. Introduction

   • 1.1 Motivation and Importance of Studying Equilibria in Networks with Malicious Users
   • 1.2 Definition of Malicious Users and Their Impact on Network Performance
   • 1.3 Problem Definition and Key Challenges
   • 1.4 Contributions of the Paper
   • 1.5 Structure of the Paper

2. Background and Related Work

   • 2.1 Overview of Network Equilibria and Game Theory in Network Contexts
   • 2.2 Types of Malicious Users in Networks (e.g., Sybil attacks, Denial-of-Service, Free-riding)
   • 2.3 Existing Approaches to Modeling Malicious Behavior in Networks
   • 2.4 Game-Theoretic Models for Network Security and Stability
   • 2.5 Previous Work on Equilibria Analysis in Malicious Environments

3. Preliminaries

   • 3.1 Basic Concepts in Game Theory and Equilibria (e.g., Nash Equilibrium, Evolutionary Game Theory)
   • 3.2 Formal Definitions of Malicious Behavior in Network Settings
   • 3.3 Types of Network Models (e.g., Centralized, Decentralized, Peer-to-Peer)
   • 3.4 Assumptions about Users’ Behaviors and Preferences
   • 3.5 Notations and Mathematical Tools

4. Modeling Networks with Malicious Users

   • 4.1 Network Topologies and Malicious Users’ Strategic Decisions
   • 4.2 Malicious Behavior Models: Payoff Functions and Utility
   • 4.3 Incorporating Malicious Actions in Network Protocols (e.g., Routing, Resource Allocation)
   • 4.4 Incentive Mechanisms for Mitigating Malicious Behavior
   • 4.5 Malicious User Detection and Mitigation Strategies

5. Equilibria in Networks with Malicious Users

   • 5.1 Equilibrium Analysis in the Presence of Malicious Behavior
   • 5.2 Existence of Equilibria: Conditions and Challenges
   • 5.3 Pure and Mixed Strategy Nash Equilibria in Malicious Network Settings
   • 5.4 Evolutionarily Stable Strategies in the Presence of Malicious Users
   • 5.5 Cooperative vs. Non-Cooperative Behavior in Malicious Networks
   • 5.6 Game-Theoretic Frameworks for Analyzing Malicious Actions

6. Malicious Behavior and Its Impact on Network Performance

   • 6.1 Effects of Malicious Behavior on Network Stability and Efficiency
   • 6.2 Game-Theoretic Insights on the Cost of Malicious Actions (e.g., Free-riding, Misleading Routing)
   • 6.3 Impact on Social Welfare and Network Utility
   • 6.4 Strategic Behavior of Malicious vs. Non-Malicious Users
   • 6.5 Network Resilience: How Networks Adapt to Malicious Attacks

7. Designing Secure and Robust Networks

   • 7.1 Mechanisms for Preventing or Mitigating Malicious User Impact
   • 7.2 Designing Incentive-Compatible Protocols
   • 7.3 Reputation Systems and Trust Models for Detecting Malicious Users
   • 7.4 Secure Routing and Resource Allocation with Malicious Users
   • 7.5 Balancing Efficiency and Security in Network Protocols
   • 7.6 Regulatory and Policy Frameworks for Encouraging Honest Behavior

8. Experimental Results and Case Studies

   • 8.1 Methodology for Simulating Malicious User Behavior in Networks
   • 8.2 Benchmarking Equilibrium Outcomes in Malicious vs. Non-Malicious Scenarios
   • 8.3 Case Study: Malicious Behavior in Peer-to-Peer Networks
   • 8.4 Real-World Applications: Impact of Malicious Users in Cloud Computing and IoT Networks
   • 8.5 Experimental Findings on Network Stability and Performance in Malicious Environments

9. Challenges and Open Problems

   • 9.1 Scalability of Game-Theoretic Models in Large-Scale Networks
   • 9.2 Adaptive and Evolving Malicious Strategies: Implications for Equilibria
   • 9.3 Handling Multiple Malicious Users with Coordinated Attacks
   • 9.4 Security in Dynamic and Decentralized Network Architectures
   • 9.5 Open Problems in Designing Resilient and Secure Networks Against Malicious Users

10. Future Directions

• 10.1 Advancements in Behavioral Modeling for Malicious Users
• 10.2 Integration of Machine Learning for Detecting and Responding to Malicious Behavior
• 10.3 Developing Robust and Adaptable Network Protocols in Evolving Threat Environments
• 10.4 Collaborative Strategies to Counteract Malicious Behavior
• 10.5 Exploring Blockchain and Cryptographic Techniques for Securing Network Equilibria

11. Conclusion

• 11.1 Summary of Key Findings and Contributions
• 11.2 Impact of Malicious Users on Network Equilibria and Design
• 11.3 Vision for the Future of Secure and Resilient Networks

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