In this paper, a chaos-based triple-layer tree parity machine (TLTPM)-guided neural synchronization has been proposed for the development of the public key exchange protocol. A special neural network structure called tree parity machine (TPM) is used for neural synchronization. Two TPMs accept the common input and different weight vectors and update the weights using the neural learning rule by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two TPMs become identical. These identical weights serve as a secret key. There is, however, hardly any investigation to investigate the randomness of the common input vector used in the synchronization process. In this paper, logistic chaos system-based TLTPM is proposed. For faster synchronization, this proposed TLTPM model uses logistic chaos-generated random common input vector. This proposed TLTPM model is faster and has better security than TPM with the same input, output, and hidden neurons. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and robust potential.

本文提出了一种基于混沌的三层树型奇偶校验机（TLTPM）引导的神经同步技术，用于公钥交换协议的开发。称为树奇偶校验机（TPM）的特殊神经网络结构用于神经同步。两个TPM接受公共输入和不同的权重向量，并通过交换它们的输出使用神经学习规则来更新权重。在某些步骤中，它导致完全同步，并且两个TPM的权重变得相同。这些相同的权重用作秘密密钥。但是，几乎没有任何研究来研究在同步过程中使用的公共输入向量的随机性。本文提出了一种基于逻辑混沌系统的TLTPM。为了加快同步速度，该提出的TLTPM模型使用逻辑混乱生成的随机公共输入向量。与具有相同输入，输出和隐藏神经元的TPM相比，该建议的TLTPM模型更快且具有更好的安全性。这项提议的技术已通过一系列参数测试。将结果与一些最新技术进行了比较。所提出的技术的结果显示出有效和鲁棒的潜力。