Cyber-physical system knowledge graph defense method based on cloud-edge collaboration

Abstract

This invention discloses a cloud-edge collaborative knowledge graph defense method for cyber-physical systems. The method includes: addressing the difficulty in effectively integrating real-time physical state, traffic data, and non-real-time threat intelligence, as well as the limitations of cloud-edge resources, by constructing a domain ontology model under a cloud-edge collaborative architecture; on the edge side, analyzing industrial protocols and traffic based on a fingerprint database distributed from the cloud, dynamically constructing a local knowledge graph containing network behavior and physical state characteristics, performing hierarchical lifecycle management based on asset importance, and utilizing a lightweight model to achieve anomaly detection and selective reporting; on the cloud side, integrating multi-source security intelligence and edge-side graph physical topology using a large domain model to construct a global knowledge graph, and performing consistency judgment on anomalies based on physical constraints; finally, generating defense and handling suggestions based on graph retrieval enhancements. This method balances real-time performance with global correlation analysis capabilities, effectively improving the cyber-physical system's ability to perceive and defend against complex cross-domain attacks.

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