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Pattern recognition techniques (Anomaly Detection, Classification etc) to measure the performance (far, frr, eer) of the continuous authentication service.
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LICENSE
README.md
anomaly.py
anomaly_evaluation.py
classification.py
classification_maj_vote.py
classification_maj_vote_evaluation.py
extract.py
general_purpose.py
just_some_gmm_ellipses.py
main.py
read_write.py
visualize.py

README.md

Continuous Authentication Experiments

How to use

All the experiments are called from main.py file.

Load data

Firstly, you must specify from where to load the datasets. You can either load them from database (which it automatically will be saved to a json file for later use) or you can load them from a local file

  • If you want to load them from mongodb: python main.py db (ask me for mlab db credentials)
  • If you want to load them from local: python main.py local

Functions Experiments

The experiments can be set inside the main.py file, instructions of use are specified within each function

  • For pure visualization of data: visualize.my_scatter(...)

  • To experiment with pure classification: classification.experiment(...)

  • To experiment with classification OvO & Majority Vote: classification_maj_vote.experiment(...)

  • To experiment with pure anomaly/novelty detection: anomaly.experiment(...)

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