Abstract:

Eclipse attacks， as a significant threat to blockchain network layer， can isolate the attacked node from entire network by controlling its network connections， thus affecting its ability to receive block and transaction information. On this basis， attackers can also launch double-spending and other attacks， which causes substantial damage to blockchain system. To address this issue， a dynamic detection method of eclipse attacks for blockchain node analysis was proposed by incorporating deep learning models. Firstly， Node Comprehensive Resilience Index （NCRI） was utilized to represent multidimensional attribute features of the nodes， and Graph ATtention network （GAT） was introduced to update the node features of network topology dynamically. Secondly， Convolutional Neural Network （CNN） was employed to fuse multidimensional features of the nodes. Finally， a Multi-Layer Perceptron （MLP） was used to predict vulnerability of the entire network. Experimental results indicate that an accuracy of up to 89.80% is achieved by the method under varying intensities of eclipse attacks， and that the method maintains stable performance in continuously changing blockchain networks.

Key words: blockchain network layer, deep learning, eclipse attack detection, Graph ATtention network (GAT), Multi-Layer Perceptron (MLP)

摘要：

eclipse攻击作为针对区块链网络层的一种显著威胁，通过控制节点的网络连接，可导致被攻击节点与整个网络的隔离，进而影响该节点接收区块和交易信息的能力。攻击者还可以在此基础上发起双重支付等攻击，这会对区块链系统造成巨大破坏。针对该问题，结合深度学习模型，提出一种面向区块链节点分析的eclipse攻击动态检测方法。首先，利用节点综合韧性指标（NCRI）表达节点的多维属性特征，并引入图注意力神经网络（GAT）动态更新网络拓扑结构的节点特征；然后，使用卷积神经网络（CNN）融合节点的多维特征；最后，结合多层感知机（MLP）来预测整体网络的脆弱性。实验结果表明，所提方法在不同的eclipse攻击强度下的准确率最高可以达到89.80%，并且能在不断变化的区块链网络中保持稳定的性能。

关键词: 区块链网络层, 深度学习, eclipse攻击检测, 图注意力网络, 多层感知机