Open AccessThe Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations
Author(s) -
Stjepan Picek,
Annelie Heuser,
Alan Jović,
Shivam Bhasin,
Francesco Regazzoni
Publication year - 2018
Publication title -
iacr transactions on cryptographic hardware and embedded systems
Language(s) - English
Resource type - Journals
ISSN - 2569-2925
DOI - 10.46586/tches.v2019.i1.209-237
Subject(s) - computer science , hamming distance , side channel attack , machine learning , artificial intelligence , class (philosophy) , hamming code , channel (broadcasting) , algorithm , cryptography , telecommunications , decoding methods , block code
We concentrate on machine learning techniques used for profiled sidechannel 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.