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Information on cyber-related crimes, incidents, and conflicts is abundantlyavailable in numerous open online sources. However, processing the largevolumes 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 wepresent and implement a novel knowledge graph and knowledge mining frameworkfor extracting the relevant information from free-form text about incidents inthe cyberdomain. The framework includes a machine learning based pipeline forgenerating graphs of organizations, countries, industries, products andattackers with a non-technical cyber-ontology. The extracted knowledge graph isutilized to estimate the incidence of cyberattacks on a given graphconfiguration. We use publicly available collections of real cyber-incidentreports to test the efficacy of our methods. The knowledge extraction is foundto be sufficiently accurate, and the graph-based threat estimation demonstratesa level of correlation with the actual records of attacks. In practical use, ananalyst utilizing the presented framework can infer additional information fromthe current cyber-landscape in terms of risk to various entities andpropagation of the risk heuristic between industries and countries.

Knowledge mining of unstructured information: application to cyber-domain