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User Verification Based on Mouse Dynamics: a Comparison of Public Data Sets

mouse_dynamics_balabit_chaoshen_dfl

Context

Code repository of paper: 13th International Symposium on Applied Computational Intelligence and Informatics (SACI), IEEE pp. 143-147, 2019.

Used data sets

  • Balabit - Mouse Dynamics Challenge (10 users)
  • Chaoshen - Chao Shen's data set (28 users)
  • DFL - Our DFL data set (21 users)

Evaluation

Feature extraction

Raw data were segmented into mouse actions then 39 features were extracted from each mouse action. For details see

Performance measures

Performances are reported using ROC Area Under Curve (AUC).

Evaluation protocol

A binary classifier (Random forest, 100 trees) was trained for each users using positive and negative data. In the case of positive data, the chronologically first 2/3 of the data was used for training and the remaining 1/3 of the data was used for evaluation. Negative data were selected from the other users (#positive samples = #negative samples).

  • First Scenario: user identity predictions using a single mouse action
  • Second Scenario: user identity predictions using a sequence of consecutive actions (sliding window, overlap between consecutive windows was 90%).

Software: Python 3, scikit-learn 0.19.1

Usage

Example: Evaluate the Balabit data set using the first 500 actions/user and 10 actions for user identity predictions.

  1. Please set the followings in util/settings.py
  • CURRENT_DATASET = DATASET.BALABIT
  • DATASET_USAGE = DATASET_AMOUNT.FIRST1000
  • NUM_TRAINING_SAMPLES = 500
  • NUM_ACTIONS = 10
  1. Run evaluation
  • python main.py

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