Automatic Labeling for Entity Extraction in Cyber Security

Code

• github.com/stucco/auto-labeled-corpus
  OfficialIn papernone★ 0
• github.com/IS5882/Open-CyKG
  tf★ 89
• github.com/ShashSec/SMTI_SA
  none★ 0

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

Tasks

Reproductions