基于Markov攻击图和博弈模型的区块链安全态势感知方法

全面准确地感知区块链网络中各节点所遭受的日蚀攻击情况是一个难题，该文针对该难题提出一种基于Markov攻击图和博弈模型的区块链安全态势感知方法。该方法结合区块链网络各节点以及日蚀攻击的特点建立Markov攻击图模型，随后将该模型进行量化从而计算各攻击路径的转换概率，选择较高概率的攻击路径进行多阶段攻防博弈并计算双方的最大目标函数值。通过分析这些函数值，完成对整个区块链网络节点的安全态势感知，达到对未来安全情况的预测和系统维护的目的。实验对比表明，该模型方法不但具有较低的入侵成功次数，还具有较好的确保系统完整性等方面的优势。

Blockchain Security Situational Awareness Method Based on Markov Attack Graph and Game Model

It is a difficult problem to perceive comprehensively and accurately the eclipse attack of each node in the blockchain network. For this problem, this paper proposes a blockchain security situational awareness method based on the Markov attack graph and game model. The method combines the characteristics of each node of the blockchain network and the eclipse attack to establish a Markov attack graph model, then quantifies the model to calculate the conversion probability of each attack path, and selects the attack path with higher probability to conduct a multi-stage attack and defense game and calculates the maximum objective function value of both sides. By analyzing these function values, the security situation awareness of the entire blockchain network node is completed, and the purpose of predicting the future security situation and system maintenance is achieved. The experimental comparison shows that the model method not only has a low number of successful intrusions but also has the advantage of ensuring the integrity of the system.