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https://github.com/GhostPack/DeepPass
Hunting for passwords with deep learning
https://github.com/GhostPack/DeepPass
Last synced: 3 months ago
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Hunting for passwords with deep learning
- Host: GitHub
- URL: https://github.com/GhostPack/DeepPass
- Owner: GhostPack
- License: bsd-3-clause
- Created: 2022-06-01T17:40:08.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-06-01T18:06:50.000Z (over 2 years ago)
- Last Synced: 2023-11-07T16:31:58.849Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 4.34 MB
- Stars: 160
- Watchers: 5
- Forks: 29
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# DeepPass
Dockerized application that analyzes documents for password candidates.
The blogpost ["DeepPass — Finding Passwords With Deep Learning"](https://posts.specterops.io/deeppass-finding-passwords-with-deep-learning-4d31c534cd00) gives more detail on the approach and development of the model.
To run: `docker-compose up`
This will expose http://localhost:5000 where documents can be uploaded.
The API can manually be used at `http://localhost:5000/api/passwords` :
```
C:\Users\harmj0y\Documents\GitHub\DeepPass>curl -F "file=@test_doc.docx" http://localhost:5000/api/passwords
[{"file_name": "test_doc.docx", "model_password_candidates": [{"left_context": ["for", "the", "production", "server", "is:"], "password": "P@ssword123!", "right_context": ["Please", "dont", "tell", "anyone", "on"]}, {"left_context": ["that", "the", "other", "password", "is"], "password": "LiverPool1", "right_context": [".", "This", "is", "our", "backup."]}], "regex_password_candidates": [{"left_context": ["for", "the", "production", "server", "is:"], "password": "P@ssword123!", "right_context": ["Please", "dont", "tell", "anyone", "on"]}], "custom_regex_matches": null}]
```
[Apache Tika](https://hub.docker.com/r/apache/tika) is used to extract data from [various document formats](https://tika.apache.org/0.9/formats.html). [Tensorflow Serving](https://hub.docker.com/r/tensorflow/serving) is used for serving the model.
The neural network is Bidirectional LSTM:
```
embedding_dimension = 20
dropout = 0.5
cells = 200
model = Sequential()
model.add(Embedding(total_chars, embedding_dimension, input_length=32, mask_zero=True))
model.add(Bidirectional(LSTM(cells)))
model.add(Dropout(dropout))
model.add(Dense(1, activation='sigmoid'))
```
It was trained on 2,000,000 passwords randomly selected from [this leaked password list](https://crackstation.net/files/crackstation-human-only.txt.gz) and 2,000,000 extracted terms from various Google dorked documents. The stats for the .1 test set are:
```
------------------
loss : 0.04804224148392677
tn : 199446.0
fp : 731.0
fn : 3281.0
tp : 196542.0
------------------
accuracy : 0.9899700284004211
precision : 0.9962944984436035
recall : 0.983580470085144
------------------
F1 score. : 0.9898966618590025
------------------
```
The training notebook for the model is in `./notebooks/password_model_bilstm.ipynb`