Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4152 2019 2019 Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Anbar, Iraq Akeel Akeel Department of Information Technology, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Anbar, Iraq Akeel A A.Thulnoon Department of Computer Networks Systems, College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Anbar, Iraq Ahmed Mahdi Jubair The Internet of Things (IoT) has greatly changed many aspects of human life and is now a vast distributed systems network of interconnected devices that have embedded sensors; however, the battery life of these sensor nodes is limited and requires constant maintenance. Furthermore, IoT networks operating as distributed systems are vulnerable to security threats, like de-authentication and Disassociation Denial-of-Service attacks, which exploit vulnerabilities in Wi-Fi devices. While artificial intelligence, including machine learning, has been integrated into intrusion detection systems to enhance detection of cyberattacks, there is an increasing need for improved accuracy, scalability, efficiency, and IoT-specific security solutions. This paper proposed a novel model, Hybrid Optimization-based Clustering with CNN-Based De-Authentication (HOCCNN), designed to concurrently address both energy conservation and security issues in IoT-enabled heterogeneous wireless sensor networks (WSNs). The HOCCNN adopts a hierarchical clustering technique optimized using the bio-inspired Osprey Optimization Algorithm (OOA) for dynamic and energy-efficient Cluster Head (CH) selection. Additionally, we introduce a CNN model to detect and mitigate De-authentication attacks in HOCCNN by utilizing deep learning techniques and provide a more accurate threat detection solution even in the resource-constrained environment. The performance of HOCCNN was evaluated using MATLAB against existing baseline methods in terms of parameters like packet delivery ratio, network throughput, network lifetime, end-to-end delay, average energy consumption, data accuracy, and data overhead. The model demonstrates superiority over state-of-the-art baselines. Results show significant improvements. 99.1% accuracy in attack detection, 54.18% energy consumption, 6.76 s network lifetime, 0.985 packet delivery ratio, and 53.198 Mbs throughput. These results prove that HOCCNN is a complete design to achieve scalable, secure, and energy-sustainable HWSNs in IoT. 2025 2025 304 324 10.54216/JISIoT.170122 https://www.americaspg.com/articleinfo/18/show/4152