aspg-18-2073-1792028115 20261015013515 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2023 9 2 Tamarah Alaa Diame Technical Computer Engineering Department, Al-Kunooze University College,Basrah, Iraq Kadim A. Jabbar Department of Computer Engineering techniques,National University of science and technology, Thi Qar, Iraq Ahmed Taha Medical instruments engineering techniques, Al-farahidi University, Baghdad, Iraq Naseer Ali Hussien Information and Communication Technology Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq Sura Rahim Alatba Computer Technologies Engineering, Al-Turath University College, Baghdad, Iraq Mohammed Nasser Al-Mhiqani Keele University (KU), Keele, United Kingdom, Staffordshire, ST5 5AA Venkatesan Rajinikanth Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India 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. 2023 78 92 2073 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.090206 https://americaspg.com/journal/18/article/2073 https://americaspg.com/storage/21694790793.pdf