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

Abstract

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.

Authors:
 [1];  [1];  [1];  [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
Work for Others (WFO)
OSTI Identifier:
1185925
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: CISRC 2015, Oak Ridge, TN, USA, 20150408, 20150409
Country of Publication:
United States
Language:
English

Citation Formats

Jones, Corinne L, Bridges, Robert A, Huffer, Kelly M, and Goodall, John R. Towards a Relation Extraction Framework for Cyber-Security Concepts. United States: N. p., 2015. Web.
Jones, Corinne L, Bridges, Robert A, Huffer, Kelly M, & Goodall, John R. Towards a Relation Extraction Framework for Cyber-Security Concepts. United States.
Jones, Corinne L, Bridges, Robert A, Huffer, Kelly M, and Goodall, John R. Thu . "Towards a Relation Extraction Framework for Cyber-Security Concepts". United States. doi:.
@article{osti_1185925,
title = {Towards a Relation Extraction Framework for Cyber-Security Concepts},
author = {Jones, Corinne L and Bridges, Robert A and Huffer, Kelly M and Goodall, John R},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}

Conference:
Other availability
Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

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