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：本文针对区块链数字货币中双花攻击与自私挖矿两大关键挑战展开研究。双花攻击指同一笔数字货币在交易中被多次花费的问题，而自私挖矿则是通过人为篡改区块链来增加单个矿工或矿池收益的恶意行为。我们提出一种融合两种攻击的新型攻击模式，并设计了基于机器学习的风险缓解方案。具体而言，利用学习自动机这一强大的在线学习方法，开发了SDTLA与WVBM两种防御模型，可有效抵御自私挖矿攻击。实验结果表明，SDTLA方法将自私挖矿的收益阈值提升47%，而WVBM方法表现更优，已接近各矿工收益与其贡献算力比例完全匹配的理想状态。此外，通过调节Z参数，两种方法均能有效降低双花攻击风险。研究证实SDTLA与WVBM作为提升区块链网络安全性与效率的解决方案具有显著潜力。