While conventional Intrusion Detection Systems (IDS) are essential for defending against intruders in the Industrial Internet of Things (IIoT), handling data from heterogeneous and streaming data sources should receive more attention. This work introduces a novel Optimized IForest-based Intrusion Detection System (OIFIDS) which is designed to handle both heterogeneous and streaming data efficiently. The suggested approach employs a collection of optimized binary trees, each of which is trained on a distinctive subset of data, and in which the location of empty leaves determines the anomaly score assigned to a certain data point. Optimizing isolation Forest (iForest) utilizing a modified version of the Harris Hawks Optimization algorithm, which exploits both Exploration factor and Random walk strategies (ERHHO) decreases the dataset's dimension, decreases its learning time, and enhances the detection precision, accuracy, F1-score, FPR, and recall. To demonstrate how effective the proposed approach is, it is evaluated using three datasets: CICIDS-2018, NSL-KDD, and UNSW-NB15. The experimental results prove the ability of the suggested approach in handling both heterogeneous and streaming data efficiently and delivering results that were comparable to the cutting-edge baseline techniques. Moreover, it performs effectively when there are no anomalies in the training sample and when dealing with challenging scenarios with several irrelevant features and high dimensions. Based on the comparison with various state-of-the-art IDSs, the suggested approach is able to detect intrusion with greater accuracies of 95.6%, 94.8%, and 99% than the other approaches on the NSL-KDD, UNSW-NB15, and CICIDS-2018 datasets, respectively. Experiments on heterogenous data reveal that Area Under the ROC Curve (AUC) of OIFIDS beats the baseline approach for UNSW-NB15 dataset and is higher than the second-best method by 8% and 2.4% for NSL-KDD and CICIDS-2018 respectively. Evaluating the proposed system on streaming data illustrates that it can address the concept drift problem well with high AUC value of 0.948, 0.97, and 0.922 on the NSL-KDD, CICIDS-2018, and UNSW-NB15 datasets, respectively.

Renewable energy sources play a critical role in all governments’ and organizations’ energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids.

Recently, cyberattacks have been more complex than in the past, as a new cyber-attack is initiated almost every day. Therefore, researchers should develop efficient intrusion detection systems (IDS) to detect cyber-attacks. In order to improve the detection and prevention of the aforementioned cyber-attacks, several articles developed IDSs exploiting machine learning and deep learning. In this paper, a way to find network intrusions using a combination of feature selection and adoptive voting is investigated. NSL-KDD dataset, a high-dimensional dataset that has been widely used for network intrusion detection, is applied in this approach. Feature selection plays an important role for improving accuracy and testing time as it eliminates the less significant attributes from the data set, thus saving computational power and effort. The experimental results show that the proposed approach achieves an accuracy of 86.5% on the NSL-KDD test dataset using an adoptive voting algorithm trained with the selected features. In addition, the time to process each record is 97.5 microseconds, which reflects the proposed model's superior performance. Comparing the proposed model with the existing models in the literature shows that the proposed adaptive voting approach significantly improves intrusion detection accuracy, enhances computational efficiency, and reduces false positives.