ARTICLE Copyright © 2023 by American Scientific Publishers All rights reserved. Printed in the United States of America Journal of Nanoelectronics and Optoelectronics Vol. 18, pp. 338–346, 2023 www.aspbs.com/jno Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks Mohammad Khalid Imam Rahmani 1 , Moizuddin Mohammed 2 , Reyazur Rashid Irshad 3 , Sadaf Yasmin 4 , Swati Mishra 4 , Pooja Asopa 2 , Asharul Islam 5 , Sultan Ahmad 6, 7, ∗ , and Aleem Ali 8 Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and func- tions. But, it has the critical task to detect the malicious node because node malicious attacks are dangerous attacks, and the concept of a malicious attack is opponents enter the network, search accidentally, and cap- ture one or more normal nodes. A lot of research developed to overcome this problem, but no precise results are found. In this paper, design a Hybrid Vulture and African Buffalo with Node Identity Verification (HVAB- NIV) model to predict the malicious nodes in the WSN. The fitness functions of the HVAB-NIV have operated to recognize the energy level of each node and improve the performance of node detection. The developed replica includes three stages that monitor each node, calculate the energy level and detect the malicious node. More than 100 node inputs were initialized in the proposed technique and implemented in the MAT- LAB tool. The suggested mechanism enhances the performance of malicious node detection and gains good accuracy for detecting nodes also, it saves running time and power consumption. The experimental results of the developed model has validated with other existing replicas to running time, False Prediction Rate (FPR), detection accuracy, True Prediction Rate (TPR), and power consumption. The developed methods achieve better results by gaining a high rate of accuracy detection, less running time, and false rate detection. Keywords: Wireless Sensor Network, Sensor Node, Malicious Node, Energy Value, Moving Nodes, Monitoring Module, Vulture Optimization, African Buffalo Optimization. 1 College of Computing and Informatics, Saudi Electronic University, Riyadh-11673, Saudi Arabia 2 Department of Computer Science, Banasthali Vidyapith, Niwai 304021, Rajasthan, India 3 Department of Computer Science, College of Science and Arts, Najran University, Sharurah-98341, Najran, Saudi Arabia 4 International School of Business Management, Suresh Gyan Vihar Uni- versity, Jaipur 302029, India 5 College of Computer Science, King Khalid University, Abha-62217, Saudi Arabia 6 Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj-11942, Saudi Arabia 7 Department of Computer Science and Engineering, University Center for Research and Development (UCRD), Chandigarh University, Gharuan, Mohali 140413, Punjab, India 8 Department of Computer Science and Engineering, UIE, Chandigarh University, Mohali, Chandigarh 140413, Punjab, India ∗ Author to whom correspondence should be addressed. Email: s.alisher@psau.edu.sa 1. INTRODUCTION WSNs typically have several nodes that transfer data from the source to the destination. Attacks are impacted by the transmission link of the node during the communication process, affecting the WSN [1]. Many methods have cre- ated to solve these issues, but they have specific issues with identifying fraudulent nodes. The primary concern in wireless applications is security because attacks in the WSN are volatile. Due to malicious node attacks, the WSN affects major harm during data sharing [2, 3]. The mali- cious node attack is dangerous it restricts the sensor nodes or good nodes. After that, start data broadcasting, and it abruptly stops. These issues prompted this study into the identification of malicious attacks in WSN. In general, Wireless Sensor Networks (WSN) has employed to track variables present in the WSN trans- mission environment [1]. A gateway has used to transport Received: 11 September 2022 Accepted: 30 January 2023 338 J. Nanoelectron. Optoelectron. 2023, Vol. 18, No. 3 1555-130X/2023/18/338/009 doi:10.1166/jno.2023.3397