基于学习自动机的降低自私挖矿和双花攻击风险的新型智能防御系统
New intelligent defense systems to reduce the risks of Selfish Mining and Double-Spending attacks using Learning Automata
AI总结：
针对区块链数字货币的双花与自私挖矿攻击及二者结合的新型攻击，提出基于学习自动机的SDTLA和WVBM防御模型，可提升自私挖矿盈利阈值、降低双花风险，优化区块链安全与效率。
摘要：
本文针对基于区块链的数字货币中双花攻击和自私挖矿攻击的关键挑战展开研究。双花是指数字货币交易中同一货币被多次使用的问题，自私挖矿则是指故意篡改区块链以提高单个矿工或矿工群体收益的行为。本文提出了一种结合上述两种攻击的新型攻击方式，并提出了一种基于机器学习的解决方案以缓解相关风险。具体而言，我们采用学习自动机（一种强大的在线学习方法）开发了SDTLA和WVBM两个模型，可有效防御自私挖矿攻击。实验结果表明，SDTLA方法将自私挖矿的盈利阈值提升至47%，WVBM方法表现更优，其效果非常接近“每个矿工的收益与其共享哈希算力成正比”的理想状态。此外，我们证明两种方法均可通过调整Z参数有效降低双花攻击风险。研究结果凸显了SDTLA和WVBM作为提升区块链网络安全性与效率的可行方案的潜力。
英文摘要：
In this paper, we address the critical challenges of double-spending and selfish mining attacks in blockchain-based digital currencies. Double-spending is a problem where the same tender is spent multiple times during a digital currency transaction, while selfish mining is an intentional alteration of a blockchain to increase rewards to one miner or a group of miners. We introduce a new attack that combines both these attacks and propose a machine learning-based solution to mitigate the risks associated with them. Specifically, we use the learning automaton, a powerful online learning method, to develop two models, namely the SDTLA and WVBM, which can effectively defend against selfish mining attacks. Our experimental results show that the SDTLA method increases the profitability threshold of selfish mining up to 47$\%$, while the WVBM method performs even better and is very close to the ideal situation where each miner's revenue is proportional to their shared hash processing power. Additionally, we demonstrate that both methods can effectively reduce the risks of double-spending by tuning the $Z$ Parameter. Our findings highlight the potential of SDTLA and WVBM as promising solutions for enhancing the security and efficiency of blockchain networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
