As Industrial Control Systems (ICSs) increasingly adopt cloud–edge collaborative architectures, they face escalating risks from complex cross-domain cyber threats. To address this challenge, we propose a novel threat assessment framework specifically designed for cloud–edge-integrated ICSs. Our approach systematically identifies and evaluates security risks across cyber and physical boundaries by building a structured dataset of ICS assets, attack entry points, techniques, and impacts. We introduce a unique set of evaluation indicators spanning four key dimensions—system modules, attack paths, attack methods, and potential impacts—providing a holistic view of cyber threats. Through simulation experiments on a representative ICS scenario inspired by real-world attacks, we demonstrate the framework’s effectiveness in detecting vulnerabilities and quantifying security posture improvements. Our results underscore the framework’s practical utility in guiding targeted defense strategies and its potential to advance research on cloud–edge ICS security. This work not only fills gaps in the existing methodologies but also provides new insights and tools applicable to sectors such as smart grids, intelligent manufacturing, and critical infrastructure protection.

The results of this study highlight the effectiveness of the proposed semantic security detection framework, SSB, in identifying a wide range of vulnerabilities in smart contracts tailored for industrial control scenarios. Compared to existing tools like ZEUS, Securify, and VULTRON, SSB demonstrates superior logical coverage across various vulnerability types, as evidenced by its performance on smart contract samples. This suggests that semantic-based approaches, which integrate domain-specific invariants and runtime monitoring, can address the unique challenges of ICS, such as real-time constraints and semantic consistency between code and physical control logic. The framework’s ability to model industrial invariants—covering security, functionality, consistency, time-related, and resource consumption aspects—provides a robust mechanism to prevent critical errors like unauthorized access or premature equipment operation. However, the lack of real-world ICS validation due to confidentiality constraints limits the generalizability of these findings. Future research should focus on adapting SSB for real industrial deployments, exploring scalability across diverse ICS architectures, and integrating advanced AI techniques for dynamic invariant adjustment. Additionally, addressing cross-chain interoperability and privacy concerns could further enhance the framework’s applicability in complex industrial ecosystems.