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