Securing the Internet of Things-enabled smart city infrastructure using a hybrid framework

Abstract

Since the Internet of Things (IoT) employs a diverse range of new technologies, it is impossible to build a single recommended design adopted as a master plan for all possible requests. Some possible IoT application areas have not been looked into yet or do not have enough information on how to approach them. This shows that more research needs to be done in this difficult area to find new and potentially big benefits for society. Although smart cities offer residents and providers of capital several advantages, there are numerous ways that breaches could compromise the safety and security of individuals. As a result, several different recommendation designs can coexist in the IoT. This research examines the effects of ethics and technology on the security of IoT-enabled systems in smart city infrastructure. Hence provides a secure IoT network architecture for smart cities combining blockchain and deep learning to safeguard privacy and credibility. This research presents a Secure Smart City Infrastructure using Blockchain and Deep Learning (SSCI-BDL) framework to ensure privacy protection and trustworthiness among IoT communication in smart cities. This framework involves the blockchain network for security management in the smart city infrastructure. This framework integrates the deep learning model with an optimization algorithm that maintains efficient resource utilization in the smart city infrastructure. The simulation results show that the system has high security of 99.5% and the lowest latency rate of 4.1% compared to existing models. Overall, the proposed framework’s efficiency gives the highest rate of 99.8%.