As an emerging and prospective paradigm, the industrial Internet of Things (IIoT) enable intelligent manufacturing through the interconnection and interaction of industrial production elements. The traditional approach that transmits data in a single physical network is undesirable because such a scheme cannot meet the network requirements of different industrial applications. To address this problem, in this article, we propose a network slicing orchestration system for remote adaptation and configuration in smart factories. We exploit software-defined networking and network functions virtualization to slice the physical network into multiple virtual networks. Different applications can use a dedicated network that meets its requirements with limited network resources with this scheme. To optimize network resource allocation and adapt to the dynamic network environments, we propose two heuristic algorithms with the assistance of artificial intelligence and the theoretical analysis of the network slicing system. We conduct numerical simulations to learn the performance of the proposed algorithms. Our experimental results show the effectiveness and efficiency of our proposed algorithms when multiple network services are concurrently running in the IIoT. Finally, we use a case study to verify the feasibility of the proposed network slicing orchestration system on a real smart manufacturing testbed.

Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating them with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder–decoder (STAGED) model for industrial fault diagnosis. First, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multiseries. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on the Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.

Industrial Internet of Things (IIoT) is vulnerable to advanced persistent threat (APT). In this article, we study a scenario in which APT is launched to attack IIoT devices. Considering the APTs lateral movement, a node-level state evolution model is established to calculate the probability of every device in an IIoT system to be compromised by APT. Based on this, a Stackelberg game model is proposed for the APT attacker and defender, which can accurately describe the gaming process. An effective computational approach is developed to obtain the potential Stackelberg equilibrium strategy pair of the game. Extensive case studies and comparison studies are conducted to validate the effectiveness of the proposed method.

Cyber-physical security has been becoming an important issue for many critical infrastructures in human society, including electrical power grids. This paper proposes an impact assessment framework for smart grids suffering false data injection attack against smart meters. Firstly, the propagation model of malicious false data codes in smart meter communication networks is established. Based on this, a security-constrained economic dispatch model is formulated to assess how false data codes would mislead the grid operator's actions. A defense strategy is proposed, which enhances the grid's vulnerability through effectively deploying defense resources in smart meter networks. Extensive numerical simulations are conducted to evaluate the impact of different attack scenarios as well as the effectiveness of the defense strategy.

Defending microgrids against cyberattacks has been recognized as a significant task in modern energy systems. This paper studies a False Data Injection Attack (FDIA) scenario targeting microgrids, in which the attacker maliciously propagates false data malware in the communication network of a microgrid system, aiming at misleading the microgrid’s operation. Firstly, this paper establishes the propagation and attack models for the false data malware; then, the impact of the FDIA is quantified through the formulation of a network-constrained microgrid dispatch model. Based on this, a false data malware propagation-aware game between the attacker and the defender is established and an effective computational approach is developed for solving the proposed game and obtaining the potential Nash equilibrium strategy pair. Extensive numerical simulations are conducted to validate the proposed framework.