Automatic Labeling for Entity Extraction in Cyber Security

Conference · Wed Jan 01 04:00:00 EST 2014

OSTI ID:1143555

Bridges, Robert A ^[1]; Jones, Corinne L ^[1]; Iannacone, Michael D ^[1]; Testa, Kelly M ^[1]; Goodall, John R ^[1]

---

1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL)
• Sponsoring Organization: ORNL work for others
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1143555
• Country of Publication: United States
• Language: English

Related Subjects

Automatic Labeling
Averaged Perceptron
Cyber Security
Entity Extraction
Maximum Entropy Model