2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 304-324, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Foad Salem Mubarek 1 , Akeel A.Thulnoon 2 * , Ahmed Mahdi Jubair 3
Doi: https://doi.org/10.54216/JISIoT.170122
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 Mb/s throughput. These results prove that HOCCNN is a complete design to achieve scalable, secure, and energy-sustainable HWSNs in IoT.
Internet of Things (IoT) , Heterogeneous Wireless Sensor Networks , Hybrid Optimization , CNN-Based De-Authentication , Osprey Optimization Algorithm
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