Papers › Automatic Labeling for Entity Extraction in Cyber Security
Automatic Labeling for Entity Extraction in Cyber Security
Robert A. Bridges, Corinne L. Jones, Michael D. Iannacone, Kelly M. Testa, John R. Goodall
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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