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

Stjepan Picek, Annelie Heuser, Alan Jovic, Shivam Bhasin, Francesco Regazzoni

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

We concentrate on machine learning techniques used for profiled side-channel analysis in the presence of imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight or Hamming distance leakage models. In order to deal with the imbalanced data, we use various balancing techniques and we show that most of them help in mounting successful attacks when the data is highly imbalanced. Especially, the results with the SMOTE technique are encouraging, since we observe some scenarios where it reduces the number of necessary measurements more than 8 times. Next, we provide extensive results on comparison of machine learning and side-channel metrics, where we show that machine learning metrics (and especially accuracy as the most often used one) can be extremely deceptive. This finding opens a need to revisit the previous works and their results in order to properly assess the performance of machine learning in side-channel analysis.
Original languageEnglish
Pages (from-to)209-237
Number of pages29
JournalIACR Transactions on Cryptographic Hardware and Embedded Systems
Volume2019
Issue number1
DOIs
Publication statusPublished - 2018

Keywords

  • Profiled side-channel attacks
  • Imbalanced datasets
  • Synthetic examples
  • SMOTE
  • Metrics

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