basel / ATTACK-BERT

Run on huggingface.co

huggingface.co

Total runs: 5.3K

24-hour runs: -36

7-day runs: -11.6K

30-day runs: -18.3K

Model's Last Updated: January 15 2025

sentence-similarity

Introduction of ATTACK-BERT

Model Details of ATTACK-BERT

ATT&CK BERT: a Cybersecurity Language Model

ATT&CK BERT is a cybersecurity domain-specific language model based on sentence-transformers . 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.

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

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

Runs of basel ATTACK-BERT on huggingface.co

5.3K

Total runs

-36

24-hour runs

-226

3-day runs

-11.6K

7-day runs

-18.3K

30-day runs

More Information About ATTACK-BERT huggingface.co Model

ATTACK-BERT huggingface.co

ATTACK-BERT huggingface.co is an AI model on huggingface.co that provides ATTACK-BERT's model effect (), which can be used instantly with this basel ATTACK-BERT model. huggingface.co supports a free trial of the ATTACK-BERT model, and also provides paid use of the ATTACK-BERT. Support call ATTACK-BERT model through api, including Node.js, Python, http.

ATTACK-BERT huggingface.co Url

https://huggingface.co/basel/ATTACK-BERT