https://github.com/taxborn/continuous-authentication

Experiments in continuous authentication
https://github.com/taxborn/continuous-authentication

Last synced: 5 days ago
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Experiments in continuous authentication

• Host: GitHub
• URL: https://github.com/taxborn/continuous-authentication
• Owner: taxborn
• Created: 2023-12-07T00:04:24.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-29T22:43:36.000Z (12 months ago)
• Last Synced: 2023-12-29T23:31:06.402Z (12 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 9.87 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Continuous Authentication Experimentation

I wanted to extend my research done on [taxborn/mauth-research-project](https://github.com/taxborn/mauth-research-project) to

clean up the code, and implement some ideas I didn't get to investigate.

The dataset I am using is the same dataset from that repository for now, but hope to extend to other datasets in the future.

# Running the models

All you need to run the models is to ensure you have the requirements installed in [requirements.txt](./requirements.txt), and execute `train.py`.

```

pip install -r requirements.txt && python train.py

```

There is also a Jupyter Notebook at [ca-visualization.ipynb](./ca-visualization.ipynb), which has an additional dependency of `matplotlib`.

This contains some visualizations of validations of the models.

## Implemented

- Preprocessing for the [mauth dataset](https://github.com/taxborn/mauth-research-project).

- Training KNearestNeighbors Classifier & Regressor

    - The regressor is used in computing *trust score*, metric of trust the system has in the current user

- Type-checked with [mypy](https://www.mypy-lang.org/)!

## TODO

- Create a script for data collection

    - Compute features ***continuously***.

    - Run against the model ***continuously***.

- Experiment with different trust score equations

- Configurable if this is deterministic or not (e.g. `random_state`)