With the rapid advances in Internet of Things (IoT) technologies, the number of smart objects connected to IoT networks is increasing day by day. Parallel to this exponential growth, attacks against IoT networks are also increasing rapidly. Various Intrusion Detection Systems (IDS) have been proposed by researchers to improve accuracy in detecting attacks with different behaviors and reduce intrusion detection time. This work presents a novel IDS based on the combination of the Principal Component Analysis and Mayfly Optimization methods (PCA-MAO) for dimensionality reduction, the Borderline Synthetic Minority Oversampling Technique (BSMOTE) for data balancing, and the Long Short-Term Memory (LSTM) method for classification. A new dataset was created by combining IoTID20, CIC-ToN-IoT and USB-IDS-1 datasets to be used in the performance test of the proposed model. Thus, the performance evaluation of the proposed model was performed for more attack types with different behaviors. As a result of classification using the proposed hybrid PCA-MAO based LSTM model, an accuracy of 99.51% was obtained. It has been observed that the proposed IDS provides superior intrusion detection performance for high-dimensional, complicated, and imbalanced data compared to classical machine learning (ML) methods.
In the successful maintenance of electricity generation management processes in power generation plants, it is of great importance to determine the risks that may arise during the operation of the relevant processes, take measures to minimize these risks, and take the necessary actions. In this study, common risks in the electricity generation management process in HEPPs were identified and these risks were rated by experts (decision-makers) within each power plant itself. Since this rating is made by the experts of each power plant, the impact and probability values of the same risk may differ, and accordingly, different risk levels may arise for the same risk. In the study, the SWARA method was used to compare the risk levels of common risks in the electricity generation process in different power plants and calculate the final weight values of the related risks. As a result of the measures determined for each risk in the electricity generation management processes in the power plants and the actions taken for these measures, it was determined whether the relevant risks were reduced to acceptable levels by looking at the results of the internal audits. In the internal audits, the performance of HEPPs in eliminating the related risks is evaluated with fuzzy expressions separately for each risk. The risk weight values obtained by the SWARA method and the fuzzy expressions obtained as a result of the risk assessment were analyzed with the Fuzzy TOPSIS method, and the performance values of the power plants in eliminating the risks were calculated, then the performance ranking was made in the light of these values.
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