  Information Security in the cyber world is a major cause for concern, with
significant increase in the number of attack surfaces. Existing information on
vulnerabilities, attacks, controls, and advisories available on the web
provides an opportunity to represent knowledge and perform security analytics
to mitigate some of the concerns. Representing security knowledge in the form
of ontology facilitates anomaly detection, threat intelligence, reasoning and
relevance attribution of attacks, and many more. This necessitates dynamic and
automated enrichment of information security ontologies. However, existing
ontology enrichment algorithms based on natural language processing and ML
models have issues with the contextual extraction of concepts in words, phrases
and sentences. This motivates the need for sequential Deep Learning
architectures that traverse through dependency paths in text and extract
embedded vulnerabilities, threats, controls, products and other security
related concepts and instances from learned path representations. In the
proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and
Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder was deployed
to enrich ISO 27001 based information security ontology. The approach yielded a
test accuracy of over 80\% when tested with knocked out concepts from ontology
and web page instances to validate the robustness.
