Industrial Internet identifiers have the characteristics of heterogeneity, off-site, and foreign ownership. Traditional identifier systems encountered many problems, such as IP address exhaustion or difficulty in addressing and routing, which cannot meet the demands of numerous heterogeneous and interoperable Industrial Internet objects. At the same time, they also face various security risks. In this article, we propose multi-identifier network for Industrial Internet of Things (MIN-IIoT), a universal identifier system named multi-identifier network architecture for Industrial Internet, which supports the coexistence of multiple identifiers, such as identity, content names, services, locations, and IP addresses. MIN-IIoT establishes a unified identifier system and puts forward a series of identifier management methods, including identifier registration, addressing, mutual translation, and route forwarding. In addition, all identifier information is stored in a hierarchical consortium blockchain to ensure that they are authentic and immutable. Identity authentication, Proof of Vote (PoV)-based consensus algorithm, and trusted computing technology effectively protect the security of MIN-IIoT. Meanwhile, MIN-IIoT is convenient for deployment in various complex Industrial field environments. The extensive experiments show that MIN-IIoT has better performance in identifier management of Industrial Internet than traditional methods.

Recently, more and more mobile devices have been connected to the Internet. The Internet environment is complicated, and network security incidents emerge endlessly. Traditional blocking and killing passive defense measures cannot fundamentally meet the network security requirements. Inspired by the heuristic establishment of multiple lines of defense in immunology, we designed and prototyped a Double Defense strategy with Endogenous Safety and Security (DDESS) based on multi-identifier network (MIN) architecture. DDESS adopts the idea of a zero-trust network, with identity authentication as the core for access control, which solves security problems of traditional IP networks. In addition, DDESS achieves individual static security defense through encryption and decryption, consortium blockchain, trusted computing whitelist, and remote attestation strategies. At the same time, with the dynamic collection of data traffic and access logs, as well as the understanding and prediction of the situation, DDESS can realize the situation awareness of network security and the cultivation of immune vaccines against unknown network attacks, thus achieving the active herd defense of network security.

The network security in the industrial control system of nuclear power enterprises is related to nuclear power security, and its own characteristics put forward higher requirements for the safety protection system. The traditional passive defense measures of “blocking and killing” cannot fundamentally solve the safety requirements of the industrial control system, which has led to the continuous emergence of nuclear power system safety accidents due to network attacks. Trusted computing technology can timely discover the ongoing network attacks, predict and identify potential attacks through situation awareness and other technologies, and take corresponding measures to block attacks. Application scenarios of Trusted Computing 3.0 in the industrial control system of nuclear power enterprises are discussed, including the overall framework of the system, the construction of trusted nodes, the deployment of the system, and the creation of the whole security system. It provides a powerful guarantee for the safe operation of the industrial control system of nuclear power enterprises.

To detect and resolve distributed deadlocks in the generalized model, a few algorithms have been proposed. Most of them are distributed algorithms which are based on the diffusing computation technique where propagation of probes and backward propagation of replies are required. On the contrary, centralized algorithms send the dependency information directly to the initiator. However, existing algorithms do not distinguish local deadlocks from global deadlocks. In this paper, we present a semi-centralized algorithm which effectively detects and resolves local deadlocks prior to global deadlocks. To be specific, a consistent snapshot of dependency information of involved nodes is recorded in an information table at each site. Therefore, the detection and resolution of local deadlocks can be performed at each site before the dependency information is sent to the initiator to further detect global deadlocks. The proposed algorithm also handles the problem of phantom edges and concurrent executions. Performance analysis shows that the performance of our algorithm is close to or better than that of the existing algorithms in terms of execution time, message traffic and message size.