# Automatic Labeling for Entity Extraction in Cyber Security

arXiv: https://arxiv.org/abs/1308.4941 · HTML: https://mlsanity.com/paper/1308.4941/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2013.

## Built on by (most cited first, 13 of 13)

- [Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI](https://mlsanity.com/paper/2211.01753/) (2022)
- [What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey](https://mlsanity.com/paper/2109.06808/) (2021)
- [Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models](https://mlsanity.com/paper/2108.06590/) (2021)
- [From Threat Reports to Continuous Threat Intelligence: A Comparison of Attack Technique Extraction Methods from Textual Artifacts](https://mlsanity.com/paper/2210.02601/) (2022)
- [Exploring the Limits of Transfer Learning with Unified Model in the Cybersecurity Domain](https://mlsanity.com/paper/2302.10346/) (2023)
- [Graphene: Infrastructure Security Posture Analysis with AI-generated Attack Graphs](https://mlsanity.com/paper/2312.13119/) (2023)
- [AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports](https://mlsanity.com/paper/2404.07765/) (2024)
- [CPE-Identifier: Automated CPE identification and CVE summaries annotation with Deep Learning and NLP](https://mlsanity.com/paper/2405.13568/) (2024)
- [Recognizing and Extracting Cybersecurtity-relevant Entities from Text](https://mlsanity.com/paper/2208.01693/) (2022)
- [Constructing a Knowledge Graph from Textual Descriptions of Software Vulnerabilities in the National Vulnerability Database](https://mlsanity.com/paper/2305.00382/) (2023)
- [LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition](https://mlsanity.com/paper/2409.10521/) (2024)
- [Context-aware Entity-Relation Extraction for Threat Intelligence Knowledge Graphs](https://mlsanity.com/paper/2605.15904/) (2026)
- [Automated Mapping of CVE Vulnerability Records to MITRE CWE Weaknesses](https://mlsanity.com/paper/2304.11130/) (2023)

## Abstract (arXiv)

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.