一种基于LSTM-Blacklist的动态信任度证明机制

A Mechanism of Proof-of-Dynamic-Trust Based on LSTM and Blacklist

针对区块链网络中共识节点的恶意行为导致的区块链系统安全问题,提出一种基于LSTM(long short-term memory)-Blacklist的动态信任度证明机制(PoDT-LSTMB)。该动态信任度证明机制通过前向注意力机制的两层LSTM神经网络学习并分析参与共识节点的行为数据,预测节点行为倾向。以节点信任度为基础构建黑名单,剔除低于信任度阈值的节点,提高全网节点的总体可信性。以正常区块上链率以及节点信任度的变化为主要评估指标,与信任度证明PoT(Proof of Trust)机制以及不带黑名单的PoDT-LSTM机制进行了对比实验。实验结果表明,基于前向注意力机制的两层LSTM神经网络结构准确率可达0.9151,本文提出的PoDT-LSTMB机制比PoT机制的正常区块上链率提高30%~33%。

Aiming at the security problem of blockchain system caused by the malicious behavior of consensus nodes in the blockchain network,a dynamic trust proof mechanism(PoDT-LSTMB)based on LSTM(long short-term memory)and Blacklist is proposed.The dynamic trust proof mechanism learns and analyzes the behavior data of participating consensus nodes through the two-layer LSTM neural network of the forward attention mechanism,and predicts the behavior tendency of nodes.A blacklist is built based on node trust,eliminating nodes below the trust threshold to improve the overall trust of nodes in the entire network.Taking the normal block chaining rate and the change of node trust as the main evaluation indicators,we conducted comparative experiments with the PoT(Proof of Trust)mechanism and the PoDT-LSTM mechanism without blacklists.The experimental results show that the accuracy of the two-layer LSTM neural network based on the forward attention mechanism can reach 0.9151.The PoDT-LSTMB mechanism proposed in this paper improves the chain-up ratio of normal block by 30%~33%over the PoT mechanism.