Abstract: With the emergence of the network and cross-border characteristics of organized crime, the use of open source intelligence to conduct cybercrime big data association analysis based on knowledge graphs and criminal identification based on social network analysis can effectively combat criminal acts and minimize the living space of criminals. Taking "Cross-border Telecom Fraud Criminal Governance" as an example, use open-source data to construct a knowledge graph of cross-border telecom fraud events and a network of stakeholders in each event to identify the core figures of criminal groups and potential criminals. Most cybercrimes commit crimes in the form of criminal gangs or groups. In order to avoid supervision and combat, criminals gradually transfer criminal dens and communication tools abroad, and purposefully implement cross-border networks against Chinese citizens crime. Graphing cases, incidents, etc., and building large-scale knowledge nodes and relationship links, can effectively discover hidden personnel, funds, technology, information flows, etc., and help the public security department to adopt precise combat methods to curb criminal behavior.

Key words: Open source intelligence, Cross-border crime governance, Cybercrime, Incident knowledge graph, Social network analysis