Abstract
The Bitcoin cryptocurrency has received much attention recently. In the network of Bitcoin, transactions are recorded in a ledger. In this network, the process of recording transactions depends on some nodes called miners that execute a protocol known as mining protocol. One of the significant aspects of mining protocol is incentive compatibility. However, literature has shown that Bitcoin mining's protocol is not incentive-compatible. Some nodes with high computational power can obtain more revenue than their fair share by adopting a type of attack called the selfish mining attack. In this paper, we propose an artificial intelligence-based defense against selfish mining attacks by applying the theory of learning automata. The proposed defense mechanism ignores private blocks by assigning weight based on block discovery time and changes current Bitcoin's fork resolving policy by evaluating branches' height difference in a self-adaptive manner utilizing learning automata. To the best of our knowledge, the proposed protocol is the literature's first learning-based defense mechanism. Simulation results have shown the superiority of the proposed mechanism against tie-breaking mechanism, which is a well-known defense. The simulation results have shown that the suggested defense mechanism increases the profit threshold up to 40\% and decreases the revenue of selfish attackers.
Abstract (translated)
比特币加密货币最近受到了广泛关注。在比特币网络中,交易是在一条日志中记录的。在这个网络中,记录交易的过程取决于一些被称为矿工的节点执行的一种协议,即矿工协议。矿工协议的一个重要方面是奖励兼容性。然而,文献表明,比特币采矿的协议不是奖励兼容的。一些具有高性能计算能力的节点可以通过采用一种称为自私采矿攻击的攻击方式来获得比他们应得更多的收入。在本文中,我们提出了一种基于人工智能的防御自私采矿攻击的方法,应用了学习自组织理论。我们提出的防御机制通过基于块发现时间分配权重来忽略私有块,并使用学习自组织理论 self-adaptively 评估分支的高度差异来改变当前比特币分叉解决政策的算法。据我们所知,我们提出的协议是文献中的第一种基于学习防御机制。模拟结果显示,我们提出的防御机制比 tie-breaking 机制优越得多,这是一项众所周知的防御方法。模拟结果显示,我们提出的防御机制可以增加利润阈值至 40%,并减少自私攻击者的收入。
URL
https://arxiv.org/abs/2301.11463
