  Information Security in the cyber world is a major cause for concern, with a
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 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 is
deployed to enrich ISO 27001-based information security ontology. The model is
trained and tested on a high-performance computing (HPC) environment to handle
Wiki text dimensionality. 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.
