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.
@article{arxiv.1308.4941,
title = {Automatic Labeling for Entity Extraction in Cyber Security},
author = {Robert A. Bridges and Corinne L. Jones and Michael D. Iannacone and Kelly M. Testa and John R. Goodall},
journal= {arXiv preprint arXiv:1308.4941},
year = {2014}
}