The implementation of the zero-trust security paradigm within Zero Trust Cloud-Edge IoT Networks poses significant challenges due to the massive scale and heterogeneity of connected devices. This difficulty can be attributed to an inherent conflict between the Zero Trust model’s requirement for granular, policy-driven controls and the inherently short-sighted nature of traditional resource orchestration strategies when managing volatile IoT workloads. Consequently, existing solutions frequently remain confined to static security postures or passive mitigation mechanisms, failing to meet the core demand for proactive adaptability essential for securing the expanding IoT system. To bridge this gap, we propose GraphSSC, an autonomous orchestration framework that leverages Knowledge Graph representation learning and Deep Reinforcement Learning to coordinate Virtual Network Functions (VNFs) as dynamic Policy Enforcement Points. Specifically, we first construct a security-aware knowledge graph that unifies semantic representations of network topology, heterogeneous resources, and critical zero-trust context. Building upon this semantic foundation, we develop a context-aware graph attention network to facilitate precise learning of service request embeddings and real-time network security posture. Subsequently, a deep reinforcement learning agent is trained to derive proactive orchestration policies. These strategies enhance security posture, reduce performance overhead, and improve resource utilization efficiency while enabling granular traffic access control through dynamically deployed VNFs. Extensive simulation results demonstrate that, in comparison to existing state-of-the-art benchmark solutions, GraphSSC significantly increases acceptance rates to over 97% and achieves a long-term benefit-cost ratio exceeding 0.6. This research offers a scalable intelligent solution for next-generation zero-trust IoT networks, thereby enabling autonomous proactive security orchestration capabilities.

Dynamic and effective trust management is an important quality assurance component for web-based cloud services. Trust management provides a quality assessment by evaluating the trustworthiness of a service. Traditional evaluation methods do not consider the factors that determine trust in multiple aspects, while ignoring the stealthiness of trust-related attacks. To address these problems, we propose DSTrust, a deep spatio-temporal network-based trust evaluation model that aggregates trust from multiple dimensions and incorporates defence mechanisms at the embedded representation layer. Our model emphasises the contextual attributes of trust and performs local and global feature attention in temporal order. Experiments show that DSTrust outperforms other methods and guarantees the reliability of the evaluation under attack.

In this paper, we propose two dependence link addition strategies to enhance the robustness of interdependent Cyber-Physical Systems. One is based on intra-degree and receiving capability difference and the other is based on intra-degree and receiving capability ratio. Numerical simulations demonstrate that the two strategies are better than adding dependence links randomly.