Recently, several research studies have used standard metaheuristic optimization algorithms rather than traditional algorithms and the Ziegler-Nichols (Z-N) method for tuning PID controller parameters. However, these studies have directly implemented these algorithms in order to configure the cascade control system one time. This paper presents a novel realtime monitoring and optimization architecture based on the Enhanced Harris Hawk Algorithm (EHHOA) and the Industrial Internet of Things (IIoT) for tuning the PID controller parameters for an Automatic Voltage Regulator (AVR) system. The EHHOA is based on a Chaotic map and an opposition-based learning technique that is linked to the IIoT layers. The proposed algorithm was implemented through Simulink in the MATLAB environment and it was compared with the Z-N method, the classical HHO/PID algorithm and the PSO/PID algorithm. The simulation results show that the proposed algorithm managed to enhance tuning with an insignificant difference in comparison with the other employed algorithms and EHHOA gave satisfactory results in adjusting the parameters of the PID controller, especially in IIoT real-time scenarios.
Security of a Wireless Sensor Network (WSN) is crucial for preventing data sharing from intruders. This paper makes a suggestion for a machine learning-based intelligent hybrid model and AI for identifying cyberattacks. The security of a Wireless Sensor Network (WSN) guards against malevolent hackers cyberattacks on data, networks, and computers. The qualities that are most closely associated to the selected attack categories are also identified using a feature reduction algorithm (SVD and PCA) and machine learning methods. In order to reduce/extract features and rank them, this paper suggests using the K-means clustering model enhanced information gain (KMC-IG). A Synthetic Minority Excessively Technique is also being introduced. Intrusion prevention systems and network traffic categorization are the eventual important stage. The study evaluates the accuracy, precision, recall, and F-measure of a proposed deep learning-based feed-forward neural network algorithm for intrusion detection and classification. Three important datasets, namely NSL-KDD, UNSW-NB 15, and CICIDS 2017, are considered, and the proposed algorithm's performance is assessed for each dataset under two scenarios: full features and reduced features. The study also compares the results of the proposed DLFFNN-KMC-IG with benchmark machine learning approaches. After dimensional reduction and balancing, the proposed algorithm achieves high accuracy, precision, recall, and F-measure for all three datasets. Specifically, for the NSL-KDD dataset in the reduced feature set, the algorithm achieves 99.7% accuracy, 99.8% precision, 97.8% recall, and 98.8% F-measure. Similarly, for the CICIDS2017 dataset, the algorithm achieves 99.8% accuracy, 98.7% precision, 97.7% recall, and 98.7% F-measure. Finally, for the UNSW-NB15 dataset, the algorithm achieves 99.1% accuracy, 98.7% precision, 98.4% recall, and 99.6% F-measure.
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