Automatic Feature Extraction

Mouse Dynamics based User Recognition using Deep Learning

Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this project we propose novel convolutional neural networks for extracting the features from the time series of users' mouse movements.

Installation

For creating a virtual environment we used Anacoda, so you have to install it for the purpose of reproductibility.

The environment.yml file contains all the necessary dependencies for the project.

Creating the environment from yml file:

conda env create -f environment.yml

Activating the environmet for usage:

conda activate tensorflow_cpu

Creating virtual environment for GPU training

conda create --name tensorflow_gpu tensorflow-gpu==1.15
conda install -c anaconda keras-gpu
conda install -c nvidia -c rapidsai -c numba -c conda-forge -c defaults cudf
conda install -c anaconda pandas
conda install -c anaconda scikit-learn
conda install -c conda-forge matplotlib
conda install -c anaconda pylint

Initial Configuration

After downloading the two separate datasets (Balabit and DFL), you need to specify these locations in the constants.py file.

Building

First of all, in the root directory, run:

pip install -e .

Using the Anaconda Prompt you simply run the command:

python ./main.py

Features

You can use a model from given neural network models, see their architectures, manage training with different methods and evaluate these results. Of course you can plot these data, make a result file as .csv format or simply print the given results to the screen.

Configuration

Define important constanst in constants.py

Define block size of given samples

BLOCK_SIZE = 128 gives the number of consecutive mouse events (1 event represents one row in the train.csv file).

Define user name

USER_NAME = 'user35' defines which user's data we want to use during measurement.

Define model for transfer learning

USED_MODEL_FOR_TRANSFER_LEARNING = 'model.h5' defines the model name in case of transfer learning.

Define train - test split value

TRAIN_TEST_SPLIT_VALUE = 0.2 defines that we want to use 20% for testing and 80% of data for training.

Training the models

• First of all you have to set sel_method = Method.TRAIN or sel_method = Method.TRANSFER_LEARNING in settings.py.

• Setting BLOCK_NUM you define how many data you want to use during the training (For example BLOCK_NUM = 50 you use 50 * BLOCK_SIZE rows from the training files).

• With sel_model you choose one of the two neural model architecture (CNN and Time Distributed).

• To defining the type of samples negative/positive balance rate you use sel_balance_type.

• There are two main type of training: identification and authentication, you set this with sel_user_recognition_type.

• To dealing with the chunk data you use drop chunks or concatenate chunks in sel_chunck_samples_handler parameter.

• With sel_train_user_number you specify that you want to traint the model for all user, or you can pick a single one from the dataset.

Evaluating the models

• First of all you have to set sel_method = Method.EVALUATE in settings.py.

• You can specify that the given dataset contains separate train and test samples using the sel_dataset_type parameter.

• With the sel_evaluation_metrics you can set the evaluation metrics from the predefined ones.

• The sel_evaluation_type specifies that you want to perform action based or session based evaluation.

Authors

Margit Antal - Sapientia Hungarian University of Transylvania Department of Mathematics– Informatics, Tirgu Mures

Norbert Fejér - Sapientia Hungarian University of Transylvania Department of Electrical Engineering, Tirgu Mures

Acknowledgements

The work was supported by Accenture Industrial Software Solutions.