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Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors

This code package is related to the paper:

M. Nerini, E. Favarelli, and M. Chiani, "Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors," IEEE Access, 2023.

Content of Code Package

The dataset.txt file contains a 5400 x 17 matrix in which:

  • each row is a sampled digit.
  • each column is a feature: the first is the pressed digit and the following are motion sensor values.

The PIN_recognition.ipynb Jupiter Notebook contains the code to replicate the results in the paper.

The files rf-prod.csv, svm-prod.csv, mlp-prod.csv, and knn-sum.csv have been obtained throguh PIN_recognition.ipynb and are attached for an easier replication of the figures.

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Machine Learning for PIN Side-Channel Attacks Based on Smartphone Motion Sensors

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