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Title: Automatic Labeling for Entity Extraction in Cyber Security

Timely analysis of cyber-security information necessitates automated information extraction from unstructured text. While state-of-the-art extraction methods produce extremely accurate results, they require ample training data, which is generally unavailable for specialized applications, such as detecting security related entities; moreover, manual annotation of corpora is very costly and often not a viable solution. In response, we develop a very precise method to automatically label text from several data sources by leveraging related, domain-specific, structured data and provide public access to a corpus annotated with cyber-security entities. Next, we implement a Maximum Entropy Model trained with the average perceptron on a portion of our corpus (~750,000 words) and achieve near perfect precision, recall, and accuracy, with training times under 17 seconds.
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  1. ORNL
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Conference: 2014 ASE International Conference on Cyber Security, Stanford, CA, USA, 20140527, 20140331
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
Work for Others (WFO)
Country of Publication:
United States
Entity Extraction; Automatic Labeling; Maximum Entropy Model; Averaged Perceptron; Cyber Security