2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 9 , Issue 2 , PP: 78-92, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Tamarah Alaa Diame 1 * , Kadim A. Jabbar 2 , Ahmed Taha 3 , Naseer Ali Hussien 4 , Sura Rahim Alatba 5 , Mohammed Nasser Al-Mhiqani 6 , Venkatesan Rajinikanth 7
Doi: https://doi.org/10.54216/JISIoT.090206
The development of power industries creates impacts on the intelligent power grids. The power grids are more valuable for transmitting information over the network. Several intermediate activities influence the networks, which are interrupted by traffic, creating network security issues. Therefore, the threats highly influence power grids, and the number of attacks also increased gradually. Several conceptual approaches are introduced to overcome the security issues; however, computation complexity is still a significant problem while detecting network anomalies. This research problem is overcome by applying the Organic Combination of Various Deep Learning (OC-VDL) approach. The introduced method observes the industry standards with the help of the Innovative Blockchain Network (IBN). During this process, IBN observes the infrastructure using the communication protocol and Manufacturing Internet of Things (IoT). The collected information is processed with the help of the Intense Autoencoder Classifier Model (IACM), which manages bilateral traffic control and helps predict abnormal activities. The effective prediction of network traffic minimizes the intermediate activities and improves the overall security up to 98.8% accuracy.
Power grids , Network Anomaly Detection , Deep Model , Intense Autoencoder Classifier Model .
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