Ensuring the reliability and trustworthiness of massive IoT-generated data processed in cloud-based systems is paramount for data integrity in IoT-Cloud platforms. The integration of Blockchain (BC) technology, particularly through BC-assisted data Edge Verification combined with a consensus system, utilizes BC's decentralized and immutable nature to secure data at the IoT network's edge. BC has garnered attention across diverse domains like smart agriculture, intellectual property, and finance, where its security features complement technologies such as SDN, AI, and IoT. The choice of a consensus algorithm in BC plays a crucial role and significantly impacts the overall effectiveness of BC solutions, with considerations including PBFT, PoW, PoS, and Ripple in recent years. In this study, I developed a Football Game Algorithm with Deep learning-based Data Edge Verification with a Consensus Approach (FGADL-DEVCA) for BC assisted IoT-cloud platforms. The major drive of the FGADL-DEVCA algorithm was to incorporate BC technology to enable security in the IoT cloud environment, and the DL model could be applied for fault detection efficiently. In the FGADL-DEVCA technique, the IoT devices encompassed considerable decentralized decision-making abilities for reaching an agreement based on the performance of the intrablock transactions. Besides, the FGADL-DEVCA technique exploited deep autoencoder (DAE) for the recognition and classification of faults in the IoT-cloud platform. To boost the fault detection performance of the DAE approach, the FGADL-DEVCA technique applied FGA-based hyperparameter tuning. The experimental result analysis of the FGADL-DEVCA technique was performed concerning distinct metrics. The experimental values demonstrated the betterment of the FGADL-DEVCA approach with other existing methods concerning various aspects.

The Internet of Things (IoT) paradigm enables end users to access networking services amongst diverse kinds of electronic devices. IoT security mechanism is a technology that concentrates on safeguarding the devices and networks connected in the IoT environment. In recent years, False Data Injection Attacks (FDIAs) have gained considerable interest in the IoT environment. Cybercriminals compromise the devices connected to the network and inject the data. Such attacks on the IoT environment can result in a considerable loss and interrupt normal activities among the IoT network devices. The FDI attacks have been effectively overcome so far by conventional threat detection techniques. The current research article develops a Hybrid Deep Learning to Combat Sophisticated False Data Injection Attacks detection (HDL-FDIAD) for the IoT environment. The presented HDL-FDIAD model majorly recognizes the presence of FDI attacks in the IoT environment. The HDL-FDIAD model exploits the Equilibrium Optimizer-based Feature Selection (EO-FS) technique to select the optimal subset of the features. Moreover, the Long Short Term Memory with Recurrent Neural Network (LSTM-RNN) model is also utilized for the purpose of classification. At last, the Bayesian Optimization (BO) algorithm is employed as a hyperparameter optimizer in this study. To validate the enhanced performance of the HDL-FDIAD model, a wide range of simulations was conducted, and the results were investigated in detail. A comparative study was conducted between the proposed model and the existing models. The outcomes revealed that the proposed HDL-FDIAD model is superior to other models.