The meteorological communication networks provide critical data support for agriculture and environmental monitoring. However, covert gradient-based attacks persistently inject subtle perturbations, threatening data integrity and increasing the operational overhead for network operators. To achieve proactive service assurance and security-aware network management, this paper proposes a data integrity monitoring mechanism as a managed network function, named EK-IGNN. Unlike traditional passive detection, EK-IGNN functions as an active security service. It first employs the Empirical Mode Decomposition Kalman Filter (EMD-KF) to extract high-fidelity attack fingerprints, which are then analyzed by an Intrinsic Graph Neural Network (IGNN). The IGNN model captures complex dependencies and adaptively amplifies weak attack features, enabling closed-loop network security management. Experimental results demonstrate that the proposed algorithm achieving an average improvement of 16.07% in accuracy and 15.27% in F1-score over state-of-the-art benchmarks.

Data sharing in the Industrial Internet of Things (IIoT) can increase productivity and reduce costs, but it is critical to ensure data security while sharing data. Secure multi-party computation (MPC) can address security problems such as data leakage during computation. However, it still faces the risk of malicious behavior from internal participants and the problem of sharing data decision-making. To address these problems, we propose a Game Theory-based secure Multi-Party Computation scheme for IIoT Data (GTMPC). Firstly, we design a traceable, verifiable and auditable industrial data sharing and computing framework for the IIoT based on blockchain. Then, we propose an MPC protocol that can resist malicious behavior from participants. It obtains the voting results through voting identifiers and multi-party multiplication. More importantly, we establish the revenue optimization models of data sharing between consumers and computing devices. Based on the above models, we model the MPC interaction-sharing process between consumers and computing devices as a Stackelberg game, and use the backward induction method to prove that there is a unique Stackelberg Equilibrium in the game. Furthermore, we also propose an MPC-solving algorithm based on the Steepest Descent Method (MPC_SDM), and obtain the optimal industrial data sharing scheme and calculation results according to the constantly updated credit score. Finally, the simulation experiment results show that the MPC_SDM algorithm is efficient and feasible in IIoT, better than the comparison algorithm, and the revenue can be increased by 13.2%.

Recently, the space TT&C networks have attracted much attention, as they can provide users with efficient and convenient information services. However, multiple independent management domains are responsible for authorization and authentication in the network, resulting in users needing to transmit many authentication messages when crossing domains. Therefore, how to efficiently authenticate users from different domains and protect their privacy has become an important issue. We propose a secure and efficient cross-domain authentication scheme (SE-CAS), which introduces blockchain to build a distributed key management architecture to establish trust between different domains and record and share parameters on domain nodes. Specifically, we propose a blockchain-based cross-domain authentication method that utilizes signatures and pseudonyms related to user identity for authentication. For massive messages from different management domains, we combine lightweight BLS signatures with group signatures to achieve batch anonymous authentication for multi-users and tasks. Security analysis shows that our scheme can authenticate requests from different domains, achieve conditional privacy-preserving, and track malicious users. Performance analysis shows that SE-CAS has lower computational overhead in cross-domain authentication, especially in handling multiple computing tasks, with higher batch authentication efficiency.