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Additive manufacturing (AM) offers numerous benefits, such as manufacturingcomplex and customised designs quickly and cost-effectively, reducing materialwaste, and enabling on-demand production. However, several security challengesare associated with AM, making it increasingly attractive to attackers rangingfrom individual hackers to organised criminal gangs and nation-state actors.This paper addresses the cyber risk in AM to attackers by proposing a novelsemantic-based threat prioritisation system for identifying, extracting andranking indicators of compromise (IOC). The system leverages the heterogeneousinformation networks (HINs) that automatically extract high-level IOCs frommulti-source threat text and identifies semantic relations among the IOCs. Itmodels IOCs with a HIN comprising different meta-paths and meta-graphs todepict semantic relations among diverse IOCs. We introduce a domain-specificrecogniser that identifies IOCs in three domains: organisation-specific,regional source-specific, and regional target-specific. A threat assessmentuses similarity measures based on meta-paths and meta-graphs to assess semanticrelations among IOCs. It prioritises IOCs by measuring their severity based onthe frequency of attacks, IOC lifetime, and exploited vulnerabilities in eachdomain.

Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems