  Additive manufacturing (AM) offers numerous benefits, such as manufacturing
complex and customised designs quickly and cost-effectively, reducing material
waste, and enabling on-demand production. However, several security challenges
are associated with AM, making it increasingly attractive to attackers ranging
from individual hackers to organised criminal gangs and nation-state actors.
This paper addresses the cyber risk in AM to attackers by proposing a novel
semantic-based threat prioritisation system for identifying, extracting and
ranking indicators of compromise (IOC). The system leverages the heterogeneous
information networks (HINs) that automatically extract high-level IOCs from
multi-source threat text and identifies semantic relations among the IOCs. It
models IOCs with a HIN comprising different meta-paths and meta-graphs to
depict semantic relations among diverse IOCs. We introduce a domain-specific
recogniser that identifies IOCs in three domains: organisation-specific,
regional source-specific, and regional target-specific. A threat assessment
uses similarity measures based on meta-paths and meta-graphs to assess semantic
relations among IOCs. It prioritises IOCs by measuring their severity based on
the frequency of attacks, IOC lifetime, and exploited vulnerabilities in each
domain.
