The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluation

The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluation

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Data sampling techniques

12 of 16

12 of 16

Data sampling techniques

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The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluation

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  1. 1 Intro
  2. 2 Big Picture
  3. 3 Labels
  4. 4 Why do we use HW?
  5. 5 Why do we care about imbalanced data?
  6. 6 What to do?
  7. 7 Random under sampling
  8. 8 Random oversampling with replacement
  9. 9 Experiments
  10. 10 Dataset 1
  11. 11 Dataset 2
  12. 12 Data sampling techniques
  13. 13 Further results
  14. 14 Evaluation metrics
  15. 15 SR/GE vs acc
  16. 16 Take away

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