With rapid development of enabling technologies such as Internet of Things, robotics, and big data, the fourth industrial revolution known as “Industry 4.0” (I4.0) has become a great opportunity for productivity innovation around the world. Industrial Internet of Things (IIoT), as a typical implication of Cyber-Physical Systems (CPS) in I4.0 era, has attracted significant attention. It aims at optimizing resources and improving production efficiency for manufacturing industry through deep interconnection of physical equipment, wide collection of multi-dimensional data, intelligent awareness of environment and automatic control. Blockchain is a kind of distributed ledger technology and has features including decentralization, anti-tamper, transparency, anonymity, and contract-autonomy. These features are helpful to improve the services and promote the development of IIoT. In this paper, we provide a comprehensive survey on the literatures applying blockchain technology into IIoT. We firstly introduce the related works and background knowledge. And then, the motivations and benefits for applying blockchain into IIoT are discussed in detail. Furthermore, based on the layered architecture of blockchain we summarized in the previous work, we outline the research framework of blockchain in IIoT. Meanwhile, the related application research is presented from the perspectives of devices security, data collection and sharing, and industrial application. Moreover, we explore the technical requirements of blockchain platforms in IIoT applications. Finally, some challenges and future directions on blockchain in IIoT, as well as broader perspectives, are concluded for further study.

As an essential piece of infrastructure supporting cyberspace security technology verification, network weapons and equipment testing, attack defense confrontation drills, and network risk assessment, Cyber Range is exceptionally vulnerable to distributed denial of service (DDoS) attacks from three malicious parties. Moreover, some attackers try to fool the classification/prediction mechanism by crafting the input data to create adversarial attacks, which is hard to defend for ML-based Network Intrusion Detection Systems (NIDSs). This paper proposes an adversarial DBN-LSTM method for detecting and defending against DDoS attacks in SDN environments, which applies generative adversarial networks (GAN) as well as deep belief networks and long short-term memory (DBN-LSTM) to make the system less sensitive to adversarial attacks and faster feature extraction. We conducted the experiments using the public dataset CICDDoS 2019. The experimental results demonstrated that our method efficiently detected up-to-date common types of DDoS attacks compared to other approaches.

In recent years, with the development and widespread use of blockchain, many projects have introduced blockchain technology to solve the increasingly serious security problems of the Industrial Internet of Things (IIoT). However, due to the conflict between the operational performance and security of the blockchain system, the conflict between transparency and privacy, and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, in this article, we propose an IIoT distributed data system based on blockchain technology. We provide a novel system architecture for different IIoT devices to deploy high-performance blockchain systems in many scenarios, such as smart factory networks. To improve the performance of the blockchain network, we adopt the sharding hashgraph consensus mechanism and introduce a node evaluation mechanism based on the state of the node, which is applied to divide a large number of nodes into many shards dynamically. We abstract the node sharding problem as a joint optimization problem and use deep reinforcement learning to solve it. Finally, we compared with asynchronous Byzantine consensus algorithms, such as HoneybadgerBFT and BEAT, which validated the performance of this system architecture.

In smart grids, the access verification of a large number of intelligent gateways and terminal devices has become one of the main concerns to ensure system security. This means that smart grids need a new key management method that is safe and efficient and has a low computational cost. Although a large number of scholars have conducted relevant research, most of these schemes cannot balance the computational overhead and security. Therefore, we propose a lightweight and secure key management method, having a low computational overhead, based on blockchain for smart grids. Firstly, we redesigned the architecture of the smart grid based on blockchain and completed the division of various entities. Furthermore, we designed a pairing-free certification authenticated group key agreement method based on blockchain under the architecture. Finally, we achieved higher security attributes, and lower authentication delay and computational overhead, compared to the traditional schemes, as shown in performance analysis and comparison.

As the Internet of Things gathers pace and popularity, more and more data is collected at the edge. To unleash the value of data and make it tradable, data markets have been proposed. However, existing data markets generally depend on broker or blockchain, which inevitably raises concerns about one or more aspects of fairness, security, or efficiency. In addition, to promote data trading in the data market, a data trading incentive mechanism is also essential. In this paper, we propose a novel data trading scheme based on state channels and Stackelberg game. First, we propose a S tate C hannels-based D ata T rading (SCDT) framework to support non-repudiable and efficient data trading. The framework can arbitrate disputes arising from off-chain data trading through state channels, enabling traders to conduct efficient transactions off-chain without worrying about security issues. Second, we propose an optimal incentive mechanism to solve the pricing and purchasing problems. The tripartite interactions among the data seller, resource seller, and user service platform are formulated as a Stackelberg game to maximize the profits of all participants. Finally, we implement the data trading framework and analyze the incentive mechanism, which reveals the feasibility of the framework and the rationality of the incentive mechanism.