In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has several advantages, including (1) it protects IIoT devices by utilizing TLVVNN synchronization to improve CEI security. (2) Here, artificial neural coordination is utilized for the exchange of neural keys between two IIoT nodes. (3) Using this suggested methodology, chaotic synchronization can be achieved, enabling the chaos-based PRNG seed exchange. (4) Vector-valued inputs and weights are taken into consideration for TLVVNN networks. (5) The deep internal architecture is made up of three hidden layers of the neural network and a vector value as input. As a result, the attacker would have great difficulty interpreting the internal structure. Experiments to verify the performance of the proposed technique are conducted, and the findings demonstrate that the proposed technique has greater performance benefits than the existing related techniques.

在本文中，提出了基于混沌引导的人工神经学习的工业物联网 (IIoT) 会话密钥协调，以增强关键能源基础设施 (CEI) 的安全性。由于数据是通过公共网络传输的，入侵者可能会带来一些安全问题。尽管已经为解决 IIoT 中的安全问题做出了大量努力，但其中大多数都依赖于传统方法。必须解决广泛的隐私问题（保密、真实性和访问控制）以保护 IIoT 系统免受攻击。由于 IIoT 节点的独特特性，现有的解决方案并没有正确解决 IIoT 网络的整个安全范围。为了解决这个问题，本文提出了一种基于混沌的三层向量值神经网络（TLVVNN）。还提出了一种基于混沌的公共种子值交换，用于在发射器和接收器处生成相同的输入向量。这种技术有几个优点，包括（1）它通过利用 TLVVNN 同步来保护 IIoT 设备以提高 CEI 安全性。(2) 在这里，人工神经协调用于两个 IIoT 节点之间的神经密钥交换。(3) 使用这种建议的方法，可以实现混沌同步，实现基于混沌的 PRNG 种子交换。(4) TLVVNN 网络考虑了向量值输入和权重。(5) 深层内部架构由神经网络的三个隐藏层和一个向量值作为输入组成。因此，攻击者将很难解释内部结构。