datasets:
- priamai/AnnoCTR
base_model:
- urchade/gliner_small-v1
tags:
- Security
- NER
- CTI
language:
- en
AITSecNER - Entity Recognition for Cybersecurity
This repository demonstrates how to use the AITSecNER model hosted on Hugging Face, based on the powerful GLiNER library, to extract cybersecurity-related entities from text.
Installation
Install GLiNER via pip:
pip install gliner
Usage
Import and Load Model
Load the pretrained AITSecNER model directly from Hugging Face:
from gliner import GLiNER
model = GLiNER.from_pretrained("selfconstruct3d/AITSecNER", load_tokenizer=True)
Predict Entities
Define the input text and entity labels you wish to extract:
# Example input text
text = """
Upon opening Emotet maldocs, victims are greeted with fake Microsoft 365 prompt that states
“THIS DOCUMENT IS PROTECTED,” and instructs victims on how to enable macros.
"""
# Entity labels
labels = [
'CLICommand/CodeSnippet', 'CON', 'DATE', 'GROUP', 'LOC',
'MALWARE', 'ORG', 'SECTOR', 'TACTIC', 'TECHNIQUE', 'TOOL'
]
# Predict entities
entities = model.predict_entities(text, labels, threshold=0.5)
# Display results
for entity in entities:
print(f"{entity['text']} => {entity['label']}")
Sample Output
Emotet => MALWARE
Microsoft => ORG
Model Details
The AITSecNER model was fine-tuned using the urchade/gliner_small model from Hugging Face on the priamai/AnnoCTR dataset. For more details about the dataset, see the paper "AnnoCTR: A Dataset for Detecting and Linking Entities, Tactics, and Techniques in Cyber Threat Reports".
GLiNER is described in detail in the paper "GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer".
About
AITSecNER leverages GLiNER to quickly and accurately extract cybersecurity-specific entities, making it highly suitable for tasks such as:
- Cyber threat intelligence analysis
- Incident response documentation
- Automated cybersecurity reporting