Advanced developments of Industrial Cyber-Physical Systems (CPSs), comprising Internet of Things (IoT) finds useful in several application areas such as transportation, smart cities, healthcare, energy distribution, agriculture, etc. At the same time, the increased utilization of industrial CPS offers many threats which could have major significances for users. Recently, cognitive computing and artificial intelligence techniques offer new opportunities for the revolution of industrial CPSs. Therefore, to achieve security in industrial CPS, AI based intrusion detection system (IDS) can be developed to detect anomalies and prevent their harmful effects. With this motivation, this paper presents a novel cognitive computing based IDS technique to achieve security in industrial CPS. The proposed model involves different stages of operations such as data acquisition, preprocessing, feature selection, classification, and parameter optimization. The proposed model involves preprocessing to discard the noise that exists in the data. Then, the presented model uses binary bacterial foraging optimization (BBFO) based feature selection technique to elect an optimal subset of features. Besides the gated recurrent unit (GRU) model is applied to identify the presence of intrusions in the industrial CPS environment. Finally, Nesterov-accelerated Adaptive Moment Estimation (NADAM) optimizer is applied for the hyperparameter optimization of the GRU model in such a way that the detection rate can be enhanced. In order to validate the performance of the BBFO-GRU model, a series of experiments were carried out using the data from industrial CPS and the resultant values highlighted the promising performance of the proposed model with an accuracy of 98.45%.

包括物联网 (IoT) 在内的工业网络物理系统 (CPS) 的先进发展在交通、智慧城市、医疗保健、能源分配、农业等多个应用领域中发挥了重要作用。工业 CPS 提供了许多可能对用户具有重大意义的威胁。最近，认知计算和人工智能技术为工业 CPS 的革命提供了新的机遇。因此，为了实现工业 CPS 的安全，可以开发基于人工智能的入侵检测系统 (IDS) 来检测异常并防止其有害影响。基于此动机，本文提出了一种基于认知计算的新型 IDS 技术，以实现工业 CPS 的安全性。所提出的模型涉及不同的操作阶段，如数据采集、预处理、特征选择、分类和参数优化。所提出的模型涉及预处理以丢弃数据中存在的噪声。然后，所提出的模型使用基于二元细菌觅食优化 (BBFO) 的特征选择技术来选择最佳特征子集。此外，门控循环单元 (GRU) 模型还用于识别工业 CPS 环境中是否存在入侵。最后，将 Nesterov 加速自适应矩估计 (NADAM) 优化器应用于 GRU 模型的超参数优化，以提高检测率。为了验证 BBFO-GRU 模型的性能，