WeRLman: To Tackle Whale (Transactions), Go Deep (RL)
Roi Bar Zur, Ameer Abu-Hanna, Ittay Eyal, Aviv Tamar
摘要
The security of proof-of-work blockchain protocols critically relies on incentives. Their operators, called miners, receive rewards for creating blocks containing user-generated transactions. Each block rewards its creator with newly minted tokens and with transaction fees paid by the users. The protocol stability is violated if any of the miners surpasses a threshold ratio of the computational power; she is then motivated to deviate with selfish mining and increase her rewards.Previous analyses of selfish mining strategies assumed constant rewards. But with statistics from operational systems, we show that there are occasional whales – blocks with exceptional rewards. Modeling this behavior implies a state-space that grows exponentially with the parameters, becoming prohibitively large for existing analysis tools.We present the WeRLman 1 framework to analyze such models. WeRLman uses deep Reinforcement Learning (RL), inspired by the state-of-the-art AlphaGo Zero algorithm. Directly extending AlphaGo Zero to a stochastic model leads to high sampling noise, which is detrimental to the learning process. Therefore, WeRLman employs novel variance reduction techniques by exploiting the recurrent nature of the system and prior knowledge of transition probabilities. Evaluating WeRLman against models we can accurately solve demonstrates it achieves unprecedented accuracy in deep RL for blockchain.We use WeRLman to analyze the incentives of a rational miner in various settings and upper-bound the security threshold of Bitcoin-like blockchains. We show, for the first time, a negative relationship between fee variability and the security threshold. The previously known bound, with constant rewards, stands at 0.25 [2]. We show that considering whale transactions reduces this threshold considerably. In particular, with Bitcoin historical fees and its future minting policy, its threshold for deviation will drop to 0.2 in 10 years, 0.17 in 20 years, and to 0.12 in 30 years. With recent fees from the Ethereum smart-contract platform, the threshold drops to 0.17. These are below the common sizes of large miners [3].
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper5
- Uncle Maker: (Time)Stamping Out The Competition in EthereumAviv Yaish, Gilad Stern, Aviv ZoharCCS 2023 · 被引用 19 次
- RICE: Breaking Through the Training Bottlenecks of Reinforcement Learning with ExplanationZelei Cheng, Xian Wu, Jiahao Yu, Sabrina Yang 等ICML 2024 · 被引用 11 次
- Mad-Dag: Protecting Blockchain Consensus From MEVRoi Bar Zur, Ittay Eyal, Aviv TamarS&P 2026 · 被引用 3 次
- Bitcoin Under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin MiningRoozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova, Bart PreneelCCS 2025 · 被引用 1 次
- How to Beat Nakamoto in the RaceShu-Jie Cao, Dongning GuoCCS 2025
它引用的顶会 Paper5
- On the Security and Performance of Proof of Work BlockchainsArthur Gervais, Ghassan O. Karame, Karl Wüst, Vasileios Glykantzis 等CCS 2016 · 被引用 1,668 次
- On the Instability of Bitcoin Without the Block RewardMiles Carlsten, Harry A. Kalodner, S. Matthew Weinberg, Arvind NarayananCCS 2016 · 被引用 387 次
- Be Selfish and Avoid Dilemmas: Fork After Withholding (FAW) Attacks on BitcoinYujin Kwon, Dohyun Kim, Yunmok Son, Eugene Y. Vasserman 等CCS 2017 · 被引用 248 次
- BDoS: Blockchain Denial-of-ServiceMichael Mirkin, Yan Ji, Jonathan Pang, Ariah Klages-Mundt 等CCS 2020 · 被引用 1 次
- SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement LearningCharlie Hou, Mingxun Zhou, Yan Ji, Phil Daian 等NDSS 2021
相关 Paper
- The Gap GameItay Tsabary, Ittay EyalCCS 2018 · 被引用 119 次
- AIRS: Explanation for Deep Reinforcement Learning based Security ApplicationsJiahao Yu, Wenbo Guo, Qi Qin, Gang Wang 等USENIX Security 2023
- Tight Consistency Bounds for BitcoinPeter Gazi, Aggelos Kiayias, Alexander RussellCCS 2020
- Fairness Matters: A Tit-For-Tat Strategy Against Selfish MiningWeijie Sun, Zihuan Xu, Lei ChenVLDB 2022 · 被引用 8 次
- BunnyFinder: Finding Incentive Flaws for Ethereum ConsensusRujia Li, Mingfei Zhang, Xueqian Lu, Wenbo Xu 等NDSS 2026 · 被引用 5 次
