סמינר: Graduate Seminar
Blockchain Analysis with Reinforcement Learning
| Blockchains secure trillions of dollars in value while operating in adversarial environments where rational actors pursue economic gain. When economic incentives fail to align with system security, critical vulnerabilities emerge. This talk examines how to identify and defend against these vulnerabilities across different decentralized systems.
First, I will present analysis of optimal selfish mining strategies, where miners manipulate the protocol for profit, particularly in scenarios where previously unexplored attack vectors are created by complex revenue structures and potential collaboration with other miners. I demonstrate how rational participants that deliberately assist attackers for their own gain risk the whole system’s security. Building on this analysis, I introduce a novel protocol that, compared to Bitcoin’s current implementation, provides stronger resistance to these collaborative selfish mining attacks. Second, I will address vulnerabilities in restaking networks, an emerging paradigm where operators use their locked capital across multiple decentralized applications simultaneously. While restaking improves capital efficiency, the stake reuse introduces new risks. I present a more robust restaking architecture that better balances the economic benefits and potential risks. Together, these contributions advance our understanding of how to align incentives with security in decentralized systems and provide practical solutions for their design. Ph.D. student Under the supervision of Prof. Ittay Eyal and Prof. Aviv Tamar.
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