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arXiv · 1308.4941

Automatic Labeling for Entity Extraction in Cyber Security

Robert A. Bridges·Corinne L. Jones·Michael D. Iannacone·Kelly M. Testa·John R. Goodall
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Abstract

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 ($\sim$750,000 words) and achieve near perfect precision, recall, and accuracy, with training times under 17 seconds.

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Robert A. Bridges, Corinne L. Jones, Michael D. Iannacone, Kelly M. Testa, John R. Goodall. 2014-06-09. Automatic Labeling for Entity Extraction in Cyber Security. https://arxiv.org/abs/1308.4941

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