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The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluation
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- 1 Intro
- 2 Big Picture
- 3 Labels
- 4 Why do we use HW?
- 5 Why do we care about imbalanced data?
- 6 What to do?
- 7 Random under sampling
- 8 Random oversampling with replacement
- 9 Experiments
- 10 Dataset 1
- 11 Dataset 2
- 12 Data sampling techniques
- 13 Further results
- 14 Evaluation metrics
- 15 SR/GE vs acc
- 16 Take away
