Article Preview
TopIntroduction
The Internet of Things (IoT) has increased worldwide thanks to the development of smart gadgets and the fifth-generation mobile communication network. It has so far been utilized in the fields of transportation, intelligent manufacturing, health care, finance, petrochemical and other businesses, and urban infrastructure, and it is having an ever-growing impact on the market (Li et al., 2021). The scale of management devices may come from millions to hundreds of organizations, enterprises, or institutions due to the widespread dissemination of IoT devices. Using the traditional method of user name and password to log in to the system is a huge amount of work and a significant risk of password leakage (Khando et al., 2021). In the IoT, access control is a security mechanism to prevent the leakage of resources, which is used to grant or cancel access rights to specific users for the specified IoT resources. The traditional centralized access control system is suitable for human-machine oriented internet scenes, the devices are in the same trust domain, which cannot meet the access control requirements of the IoT, and the traditional access control model has problems such as single point of failure (Mohammed, 2021). Aiming at this problem, blockchain technology provides a new solution for access control and security protection of the IoT. Blockchain presents a decentralized architecture for the IoT through peer-to-peer networks. Data can no longer be managed and controlled by large centralized servers. A large amount of data in the IoT will be encrypted before transmission so that users' information and privacy will be more secure.
Academics have conducted many studies on the IoT's information security and privacy protection. Ferrag et al. proposed a security and protection system for basic federated learning, one type of IoT technology, and also discussed new technologies like blockchain and malware/attack using essential federated learning as basic IoT technology. They also proposed three deep learning technologies, including a recursive neural network (RNN), a convolutional neural network (CNN), and deep neural network (DNN) with a recursive architecture. Experimental analysis of the collaborative deep learning approach for network security in IoT applications reveals that it is more effective than traditional/centralized machine learning in protecting the privacy of IoT device data and at detecting assaults (Ferrag et al., 2021). The study uses deep learning technology to ensure the privacy of the data of IoT devices. Ferrag and Shu summarized the existing investigations on the security of the IoT network blockchain, reviewed the security and privacy systems of four IoT applications based on blockchain, compared nine attributes of various consensus algorithms, including delay, throughput, calculation, storage and communication costs, scalability, attack model, advantages and disadvantages, etc. They also analyzed the performance indicators, blockchain test platform and cryptography library used in the performance evaluation of the IoT network security and privacy system based on blockchain.