| Popis: |
As Bitcoin experiences more halving events, the protocol reward converges to zero, making transaction fees the primary source of miner rewards. This shift in Bitcoin's incentivization mechanism, which introduces volatility into block rewards, could lead to the emergence of new security threats or intensify existing ones. Previous security analyses of Bitcoin have either considered a fixed block reward model or a highly simplified volatile model, overlooking the complexities of Bitcoin's mempool behavior. In this paper, we present a reinforcement learning-based tool designed to analyze mining strategies under a more realistic volatile model. Our tool uses the Asynchronous Advantage Actor-Critic (A3C) algorithm to derive near-optimal mining strategies while interacting with an environment that models the complexity of the Bitcoin mempool. This tool enables the analysis of adversarial mining strategies, such as selfish mining and undercutting, both before and after difficulty adjustments, providing insights into the effects of mining attacks in both the short and long term. Our analysis reveals that Bitcoin users' trend of offering higher fees to speed up the inclusion of their transactions in the chain can incentivize payoff-maximizing miners to deviate from the honest strategy. In the fixed reward model, a disincentive for the selfish mining attack is the initial loss period of at least two weeks, during which the attack is not profitable. However, our analysis shows that once the protocol reward diminishes to zero in the future, or even currently on days when transaction fees are comparable to the protocol reward, mining pools might be incentivized to abandon honest mining to gain an immediate profit. |
