The Cost of sybils

This thesis explores Sybil strategies in decentralized and anonymous systems, focusing on blockchain technology. Combining blockchain insights with game theory, mechanism design, and machine learning, the research analyzes Sybil attacks and their implications. It establishes a theoretical foundation by formalizing permissionless games and Sybil extension mechanisms and applies machine learning to detect scams on decentralized exchanges, showcasing automated methods for identifying Sybil-related activities. A game-theoretical analysis of Miner Extractable Value (MEV) reveals Sybil strategies’ impact on transaction ordering and economic outcomes. The research examines cost-sharing mechanisms for public goods, highlighting vulnerabilities to Sybil strategies and proposing Sybil-resistant solutions applicable to blockchain technology. This thesis enhances our understanding of Sybil strategies and their mitigation in decentralized systems, offering insights for designing more secure blockchain networks. It provides a comprehensive framework for analyzing strategic behaviors in permissionless environments, contributing to efforts to improve the robustness of decentralized systems against strategic manipulations.