An adaptive deep learning-based user authentication with digitally signed optimal key-aided encryption for secured mobile edge computing data storage in blockchain

The development of Internet of Things (IoT) gadgets boosted the need for a task computing system that is reliable and efficient. Mobile Edge Computing (MEC) is growing and has become a viable tool for proximate and dependent-on latency jobs. Edge technology is well suited to IoT applications that demand minimal latency, position understanding, and large numbers of interconnections. It certainly compensates for some deficiencies in the cloud in the fields of electrical power and immediate analysis. However, ensuring the security of data in an application context remains a significant concern. Furthermore, privacy is an issue for any computing system that contains dispersed and diverse equipment. Blockchain represents a relatively new technology that emerged as an intriguing option for ensuring integrity, safety, uniformity, and authenticity. Yet, blockchain is unable to ensure adequate privacy for information on its own. So, to prevent the privacy of IoT-based data blockchain technology was developed. The data are collected from the IoT devices. The user authentication is verified, and the data are stored in the network. Also, the Adaptive Deep Markov Random Field (ADMRF) model was used for getting the verified data. The parameters in the ADMRF are tuned with the Help of the Improved Equilibrium Optimized (IEO). Once the user authentication is verified, then the data are encrypted with the help of the Attribute-Based Encryption (ABE) technique. The encryption keys are optimally generated with the help of the IEO. The encrypted data are then digitally signed by the authorized user, and then it is stored in the blockchain. The security of the model is measured by comparing it with other existing models.