OntoEnricher: A Deep Learning Approach for Ontology Enrichment from Unstructured Text

by   Lalit Mohan Sanagavarapu, et al.

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.



page 1

page 2

page 3

page 4


A Deep Learning Approach for Ontology Enrichment from Unstructured Text

Information Security in the cyber world is a major cause for concern, wi...

RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement

Security Analysts that work in a `Security Operations Center' (SoC) play...

Predicting Network Attacks Using Ontology-Driven Inference

Graph knowledge models and ontologies are very powerful modeling and re ...

Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning

With the large volume of unstructured data that increases constantly on ...

Towards automation of threat modeling based on a semantic model of attack patterns and weaknesses

This works considers challenges of building and usage a formal knowledge...

GIANT: Scalable Creation of a Web-scale Ontology

Understanding what online users may pay attention to is key to content r...

Ontology Driven Disease Incidence Detection on Twitter

In this work we address the issue of generic automated disease incidence...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.