MDH-QCNN: Secure Data Sharing Using a Multi-objective Optimized Blockchain-Based Framework

Wireless Sensor Networks are generally a wireless network with no specific infrastructure utilized for effective monitoring of the system to ensure secure data-sharing mechanisms. Since the conventional data sharing frameworks experience node failure issues, maximum energy requirement, with reduced network lifespan, and privacy preservation, the security of the data is reduced drastically. Hence, to tackle such discrepancies, aneffective Multi-objective Detour Harris hawk-enabled Quantum Convolutional Neural Network (MDH-QCNN)-based secured data transmission mechanism is proposed. The integration of the blockchain in the research enhanced the transparent nature of the proposed secure data transmission research by ensuring data security and confidentiality. Further, the utilization of the multi-objective fitness function in the research stipulated the model's stability to detect the abnormalities. In addition, the Multi-objective Detour Harris Hawk optimization effectively fine-tuned the parameters of the model, thereby enabling the research to attain optimal convergence with reduced computational complexity. Nevertheless, the Improved Dynamic Advanced Encryption Standard (IDEAS) algorithm enhanced the security of the data by performing encryption operations using the optimized keys. The performance validation enlisted the supremacy of MDH-QCNN in ensuring data security by achieving 99.03 % accuracy in attack detection with a minimum delay of 0.01 ms for 200 nodes.