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---
pipeline_tag: sentence-similarity
tags:
- cybersecurity
- sentence-embedding
- sentence-similarity
---
# ATT&CK BERT: a Cybersecurity Language Model
ATT&CK BERT is a cybersecurity domain-specific language model based on [sentence-transformers](https://www.SBERT.net).
ATT&CK BERT maps sentences representing attack actions to a semantically meaningful embedding vector.
Embedding vectors of sentences with similar meanings have a high cosine similarity.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["Attacker takes a screenshot", "Attacker captures the screen"]
model = SentenceTransformer('basel/ATTACK-BERT')
embeddings = model.encode(sentences)
from sklearn.metrics.pairwise import cosine_similarity
print(cosine_similarity([embeddings[0]], [embeddings[1]]))
```
To use ATT&CK BERT to map text to ATT&CK techniques Check our tool SMET: https://github.com/basel-a/SMET |