A Privacy-Preserving and Secure Framework using Blockchain-based Quantum-inspired Complex Convolutional Neural Network for IoT-driven Smart Cities

The developments of IoT, smart cities have become majority of urbanization. IoT networks use the Internet as an open channel to enable distributed smart devices to collect, process data inside architecture of smart cities. In this manuscript, Privacy-Preserving and Secure Framework utilizing Blockchain-depend Quantum-inspired Complex Convolutional Neural Network for IoT-driven Smart Cities (PSF-BCH-QICCN-IoT) is proposed. Initially the dataset is gathering from BoT-IoT dataset. The gathered data is fed to block chain based Proof-of-Monitoring (PoM) for Privacy-Preserving and Secure Framework. Then feature mapping and feature selection is done by Hunger Game Search Optimization Algorithm (HGSOA). After that, QICCN is utilized for classifying anomaly likes Denial-of-Service, Distributed DoS, Normal, Reconnaissance and Theft. Generally, QICCN doesn’t show some optimization adaption techniques to determine optimum parameter to offer accurate detection. Firebug Swarm Optimization process (FSO) is proposed to enhance QICCN classifies the anomaly precisely. The performance of proposed technique is analyzed utilizing performance metrics likes accuracy, specificity, recall, precision, F1-score, false alarm rate. The proposed PSF-BCH-QICCN-IoT method attains 23.33%, 21.45% and 31.35% higher accuracy; 34.15%, 32.26% and 19.95% higher precision;25.55%, 27.35% and 22.15% higher recall analyzed to the existing methods, like developing effectual feature engineering along machine learning technique for identifying IoT-botnet cyber-attacks (DMLP -IoT-BAD), feature engineering depend performance analysis of ML-DL processes for Botnet attack identification in IoMT ( SVM– IoT-BAD) and intrusion identification scheme for IoT botnet attacks utilizing deep learning (DNN - IoT-BAD) respectively.