The IETF 6TiSCH Working Group proposed a highly reliable and low-power industrial wireless network protocol stack, which integrates IEEE 802.15.4e Time Slotted Channel Hopping(TSCH) with IPv6 protocols. Scheduling is crucial in the 6TiSCH protocol stack, as it determines when a node sends or receives network packets on specific timeslots and channels. However, the current resource scheduling suffers from random timeslot selection, channel collisions, and high energy consumption, all of which degrade network performance. In this article, we propose an innovative scheduling function consisting of three core components. First, a multi-weight quality evaluation is proposed to enable precise timeslot selection, improving resource allocation efficiency. Second, an improved Q-Learning-based scheduling mechanism is proposed to generate optimal parameters for multi-weight evaluations, which is based on the global network state implemented at the edge router. Third, a pseudo-random channel selection strategy is proposed to effectively reduce channel congestion and interference. Extensive experimental results demonstrate that our function achieves a latency improvement of up to 55% compared to state-of-the-art low-latency scheduling, and along with an enhancement of up to 6% in the packet delivery ratio.

With the widespread adoption of the Industrial Internet of Things (IIoT) and the growing complexity of industrial production processes, data interactions between IIoT domains have become increasingly frequent. Ensuring security and privacy in cross-domain authentication has thus become a critical challenge. Traditional authentication schemes face several limitations, including high computation overhead, potential privacy leakage, and vulnerability to single point of failure, making it difficult to meet the cross-domain authentication needs. To address these challenges, we propose a distributed cross-domain authentication scheme in this paper. Specifically, we first design a novel IIoT cross-domain authentication architecture based on multi-blockchain. Unlike traditional single-blockchain based architecture, this approach enables fine-grained access control for on-chain device information and provides better performance and scalability. Building on this architecture, we propose an anonymous authentication scheme utilizing Certificate-Less Public Key Cryptography (CL-PKC), ensuring identity privacy for industrial devices while maintaining low authentication resource overhead. Additionally, we adopt the Merkle hash tree to construct a novel pseudonym management mechanism, which supports efficient batch pseudonym request, verification, and revocation. Finally, we demonstrate the scheme’s advantages in security and efficiency through comprehensive security analysis and performance evaluation.

The escalating adoption of Internet of Things (IoT) technologies produces vast amounts of real-time data, which can be shared and analyzed to enhance productivity and product quality across various industries. Unfortunately, the current IoT systems rely heavily on cloud servers for centralized data management. These systems not only suffer from the single point of failure problems but also have data security issues. The malicious users may compromise user privacy or inject corrupted data during the data sharing process. In this article, we propose a secure and efficient data sharing scheme for IoT. First, the scheme integrates blockchain with the distributed database, utilizing smart contracts to ensure secure data storage, querying, and sharing in IoT. Second, we incorporate a reputation mechanism into the data sharing process. This allows IoT users to receive reputation feedback from their partners based on their behaviours, which is dynamically updated as a reputation score on the blockchain by smart contracts. Therefore, honest users get more opportunities to share data, while malicious users are held accountable and revoked from the IoT system. We also harness advanced cryptographic techniques to ensure the submission of reputation feedback does not disclose the user’s private information, preventing vindictive actions from malicious users. Finally, we demonstrate the security and effectiveness of the proposed scheme by conducting the security analysis and the detailed experiments.

In recent years, applying smart contract to Blockchain-based Service Networks (BSNs) has been considered as one of the most promising solution to boost the integration and adoption of Blockchain in big businesses. However, smart contract are especially vulnerable to attack due to poor coding. Although many existing vulnerability detection tools are restricted by rigorous rules that are defined by the experts in advance, these tools are observed to have a high false positive rate in practice. Thus we propose a vulnerability detection framework for smart contract based on the attention mechanism and data flow. The code of smart contract is transformed to a data flow according to the abstract syntax tree that is built from the code. The data flow we built with smart contract code could represent the relationships of code semantic logic. Source code, data flow, and the tags of smart contract code are used as datasets to mask processing. Then, we construct a bidirectional multi-layer transformer architecture based on the attention mechanism to train our dataset. After training, we can get the label of whether there is a vulnerability in the final smart contract. Finally, the model we proposed reaches state-of-the-art results in the practical experiments of smart contract vulnerability detection with 92.54%, 81.79%, and 86.84% in the results Accuracy, Recall, and F1score, respectively.

With the development of social networks, more and more mobile social network devices have multiple interfaces. Multipath TCP (MPTCP), as an emerging transmission protocol, can fit multiple link bandwidths to improve data transmission performance and improve user experience quality. At the same time, due to the large-scale deployment and application of emerging technologies such as the Internet of Things and cloud computing, cyber attacks against MPTCP have gradually increased. More and more network security research studies point out that low-rate distributed denial of service (LDDoS) attacks are relatively popular and difficult to detect and are recognized as one of the most severe threats to network services. This article introduces six classic queue management algorithms: DropTail, RED, FRED, REM, BLUE, and FQ. In a multihomed network environment, we perform the performance evaluation of MPTCP under LDDoS attacks in terms of throughput, delay, and packet loss rate when using the six algorithms, respectively, by simulations. The results show that in an MPTCP-enabled multihomed network, different queue management algorithms have different throughput, delay, and packet loss rate performance when subjected to LDDoS attacks. Considering these three performance indicators comprehensively, the FRED algorithm has better performance. By adopting an effective active queue management (AQM) algorithm, the MPTCP transmission system can enhance its robustness capability, thus improving transmission performance. We suggest that when designing and improving the queue management algorithm, the antiattack performance of the algorithm should be considered: (1) it can adjust the traffic speed by optimizing the congestion control mechanism; (2) the fairness of different types of data streams sharing bandwidth is taken into consideration; and (3) it has the ability to adjust the parameters of the queue management algorithm in a timely and accurate manner.

Time‐slotted channel hopping (TSCH), which can enable highly reliable and low‐power wireless mesh networks, is the cornerstone of current industrial wireless standards. In a TSCH network, all nodes must maintain high‐precision synchronization. If an adversary launches a time‐synchronization attack on a TSCH network, the entire network communication system can be paralyzed. Thus, time‐synchronization security is a key problem in this network. In this article, time synchronization is divided into single‐hop pairwise, clusterwise, and three‐level multihop according to the network scope. We deeply analyze their security vulnerabilities due to the TSCH technology itself and its high‐precision synchronization requirements and identify the specific attacks; then, we propose corresponding security countermeasures. Finally, we built a test bed using 16 OpenMoteSTM nodes and the OpenWSN software to evaluate the performance of the proposed scheme. The experimental results showed that serious security vulnerabilities exist in time‐synchronization protocols, and the proposed countermeasures can successfully defend against the attacks.