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Title: Towards a Relation Extraction Framework for Cyber-Security Concepts

In order to assist security analysts in obtaining information pertaining to their network, such as novel vulnerabilities, exploits, or patches, information retrieval methods tailored to the security domain are needed. As labeled text data is scarce and expensive, we follow developments in semi-supervised NLP and implement a bootstrapping algorithm for extracting security entities and their relationships from text. The algorithm requires little input data, specifically, a few relations or patterns (heuristics for identifying relations), and incorporates an active learning component which queries the user on the most important decisions to prevent drifting the desired relations. Preliminary testing on a small corpus shows promising results, obtaining precision of .82.
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  1. ORNL
Publication Date:
OSTI Identifier:
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Resource Relation:
Conference: CISRC 2015, Oak Ridge, TN, USA, 20150408, 20150409
Research Org:
Oak Ridge National Laboratory (ORNL)
Sponsoring Org:
ORNL work for others
Country of Publication:
United States