Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain.

Since the birth of Bitcoin, blockchain has shifted from a critical cryptocurrency technology to an enabling technology. Due to its immutability and trustworthiness, blockchain has revolutionized many fields requiring credibility and high-quality data for decision making. Particularly in business intelligence and business process management, users can use blockchain to build their blockchain-enabled collaboration and data-sharing ecosystem with their partners. In this paper, we present the development process of blockchain and consensus mechanisms, where important blockchain consensus mechanisms are introduced. The consensus mechanism is the kernel among various blockchain components to ensure security and performance. Again, we present a comparison of these consensus mechanisms from different perspectives. We take the blockchain-enabling business as an example and analyze the relationship between blockchain and business process characteristics and the ideas and principles for selecting consensus mechanisms. Finally, we describe the differences and connections among many consensus mechanisms while laying a foundation for selecting appropriate consensus mechanisms for different scenarios and fields of application.

Current Intellectual Property (IP) copyright protection technologies have low efficiency of authority management, traceability, and scalability. In this work, a blockchain-based IP copyright protection algorithm is proposed to address these issues by establishing a mathematical model of quadratic matrix transformation for IP circuit trading. This algorithm proposes the design of a distributed random embedding mechanism and position mapping function that, when IP trading occurs in blockchain, the traceable mapping function can trace the copyright information in IP trading with the mapping factor. Besides, this work analyzes the credibility, transparency, overhead, and complexity. Experimental results show that the proposed algorithm can resist replaying attacks, yet the copyright information can be rapidly retrieved after suffered from attacks. Still, the proposed algorithm has higher security, stability, and traceability.

With the development of sensor devices, wireless sensor networks have been widely used, and Wireless Body Area Networks (WBANs) are relatively common application scenarios in wireless sen-sor networks. The wearable device is used to collect physiological data of the human body, and the server device is adopted to store physiological data of the human body. The openness of the network environment and network devices' dynamic nature make WBANs vulnerable to security threats from attackers. The cen-tralized two-hop architecture contains only one hub node, and the data stored in the hub node may be tam-pered with by attackers. Once attackers occupy the node, the entire network will be paralyzed. To tackle such an issue, it is proposed in this paper a model in WBAN architecture based on Blockchain technology, where the authentication protocol and blind signature protocol between nodes are designed in the new WBAN model, making the Blockchain data transmission system in a wireless network environment secure and reliable. Experimental results show that the proposed method is promising and shows higher levels of safety and stability than other methods.

As known, the smart Grid is an essential scenario for Industry 5.0. With its rapid development, the huge number of sensors and smart devices widely used in the industrial field generate significant amounts of data that sharply increase. Facing the power grid environment with a high amount of data, it is easy to cause abnormal conditions in the power grid system and even cause the system to collapse abruptly. To tackle this problem, we simulate a layered digital twin power grid model, using the comparative learning method to process the time series in the power grid and select the positive and negative time series samples using a new slider. In the extended Convolution Neural Network (CNN), we use the Atrous Convolution model, limiting the receptive field and concentrating more data close to the dimension of the model. The time series and process classification tasks are predicted on Electricity Transformer Temperature (ETT), electricity, and University of California, Riverside (UCR) data sets, and experimental results show that the proposed method reduces the error rate of 4.38% and 5.21% in the prediction task and improves the accuracy of 8.39% and 12.73% in the classification task, indications to control the power grid model more accurately, predicting the power grid operation in the future, and take corresponding measures in time.

As one of the key components of Web 3.0, the security of cryptocurrency is essential to its development and application. A Payment Channel Network (PCN) enhances transaction confirmation speed and throughput by executing blockchain transactions off-chain. However, existing PCN protocols have problems like privacy leaks and offline and challenges in meeting privacy and security demands. Due to such problems, we propose in this work High-Capacity Virtual Channel (HCVC) scheme to achieve payment across PCNs. As a lightweight virtual channel protocol based on the unspent transaction output model, HCVC uses a bidirectional locking mechanism to lock the collateral on payment channels to increase the capacity of the virtual channel. Additionally, a bidirectional punishment mechanism is introduced to deter malicious nodes, maintain balance security, and enable secure peer-to-peer transactions across PCNs, as all phases of HCVC can be performed off-chain without confirming transactions on-chain. Experimental analysis shows that the HCVC performs better in n-path PCNs. Compared to Donner, the communication overhead of HCVC in the open and resolve(undesirable) phases can be reduced by 39% and 37%, respectively, effectively reducing the overhead of transactions and enhancing the transaction throughput of the blockchain.