The Pre-trained Foundation Models (PFMs) based on edge inference reduce communication latency from the cloud server to the user mobile devices, and support different edge nodes to provide adaptive Artificial Intelligence (AI) services to the users. The complex network environment has attracted widespread attention to the communication security of cross-domain AI services. Authentication protocol is an important way to ensure the communication security among entities. However, existing cross-domain authentication protocols have not taken into account the communication delay caused by complex cryptographic operations on the mobile devices. To address these issues, a Cross-Domain Inference Authentication (CDIA) protocol for PFMs is proposed in this paper. The complex cryptographic operations are executed collaboratively by the servers from different domains to realizes decentralized authentication. Besides, the correctness and security of CDIA protocol is proved by formal analysis. The security of CDIA protocol is verified by using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The security of negotiated group key is proved by Real-or-Random (RoR) model. Finally, the performance of CDIA protocol is evaluated to show its efficiency. Compared with related protocols, the computing cost and communication overhead of CDIA protocol are at least 52.38% and 11.9%, respectively.

The visual information processing technology based on deep learning can play many important yet assistant roles for unmanned aerial vehicles (UAV) navigation in complex environments. Traditional centralized architectures usually rely on a cloud server to perform model inference tasks, which can lead to long communication latency. Using transfer learning to unload deep neural networks to the edge-fog collaborative networks has become a new paradigm for dealing with the conflicts between computing resources and communication latency. However, ensuring the security of edge-fog collaborative networks entity remains challenging. For such, we propose an anonymous authentication and group key agreement scheme for the UAV-enabled edge-fog collaborative networks, consisting of the UAV authentication protocol and the collaborative networks authentication protocol. Utilizing the AVISPA assessment tool and security analysis, the security requirements and functional features of the proposed scheme are demonstrated. From the performance results of the proposed scheme, we show that it is superior to existing authentication schemes and promising.

In Industry 5.0, the massive number of Internet of Things devices have increasing demands for group communication with a high communication efficiency and low energy consumption. However, group communication meets continuously increasing security risk challenges. Existing authentication and group key agreement schemes have encountered many problems, such as lack of anonymity and untraceability. In this article, we propose an anonymous authentication and dynamic group key agreement scheme based on the Blockchain and token mechanism, where each group member can apply for a time-sensitive token during the first authentication and only needs to check the validity of the token in the subsequent authentication, reducing the computational and transmission costs considerably. The verification on the security of the proposed scheme is tackled through mathematical analysis and validated using ProVerif, and comparisons with existing schemes demonstrate that the proposed scheme reduces the security risks and each group member’s energy consumption.

Summary Wireless body area network (WBAN) is a special wireless mobile sensor network, which is mainly applied to the medical field. It can monitor the physical condition of patients and send this vital and sensitive information to a server that provides medical and health services. Because of the openness and mobility of WBAN, it is easier for the adversary to obtain, corrupt, or replace the data transmitted in the channel, or launch various attacks. Therefore, data security and privacy issues are the most challenging problems in WBANs. Moreover, most wearable sensors in WBAN are resource‐constrained devices, traditional security schemes may not be suitable for WBAN. Therefore, in this paper, we propose a lightweight anonymous mutual authentication and key agreement scheme for WBAN. This scheme uses only hash function operations and XOR operations. We use the automatic security verification tool ProVerif to verify the security properties of our scheme and informal security analysis is discussed. We also compared the proposed scheme with many related works and the results indicate that our scheme has either more advantages in terms of computation cost, energy consumption, and communication cost or lower security risk.