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.

翻译：摘要：本文针对区块链数字货币中的双花攻击与自私挖矿两大关键挑战展开研究。双花攻击指同一数字货币在交易中被多次使用的问题，而自私挖矿则是通过故意篡改区块链来增加单个或群体矿工收益的行为。我们提出一种结合这两种攻击的新型攻击方式，并引入基于机器学习的解决方案以降低其风险。具体而言，采用学习自动机这一强大的在线学习方法，开发了两种模型——SDTLA与WVBM，可有效防御自私挖矿攻击。实验结果表明，SDTLA方法可将自私挖矿的盈利阈值提升至47%，而WVBM方法表现更优，几乎达到理想状态——每位矿工收益与其贡献的哈希算力成正比。此外，我们证明两种方法通过调节参数Z可有效降低双花攻击风险。研究结果凸显了SDTLA与WVBM作为增强区块链网络安全性与效率的潜在解决方案。