  Information on cyber-related crimes, incidents, and conflicts is abundantly
available in numerous open online sources. However, processing the large
volumes and streams of data is a challenging task for the analysts and experts,
and entails the need for newer methods and techniques. In this article we
present and implement a novel knowledge graph and knowledge mining framework
for extracting the relevant information from free-form text about incidents in
the cyberdomain. The framework includes a machine learning based pipeline for
generating graphs of organizations, countries, industries, products and
attackers with a non-technical cyber-ontology. The extracted knowledge graph is
utilized to estimate the incidence of cyberattacks on a given graph
configuration. We use publicly available collections of real cyber-incident
reports to test the efficacy of our methods. The knowledge extraction is found
to be sufficiently accurate, and the graph-based threat estimation demonstrates
a level of correlation with the actual records of attacks. In practical use, an
analyst utilizing the presented framework can infer additional information from
the current cyber-landscape in terms of risk to various entities and
propagation of the risk heuristic between industries and countries.
