Paper 2017/1110

A Systematic Evaluation of Profiling Through Focused Feature Selection

Stjepan Picek, Annelie Heuser, Alan Jovic, and Lejla Batina


Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this assumption, here, we concentrate on the feature selection step. We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we provide a systematic evaluation of the methods of interest. The experiments are performed on several real-world data sets containing software and hardware implementations of AES, including the random delay countermeasure. Our results show that wrapper and hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected. We emphasize L1 regularization (wrapper approach) and linear support vector machine (SVM) with recursive feature elimination used after chi-square filter (Hybrid approach) that performs well in both accuracy and guessing entropy. Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise data sets, including those with countermeasures than on the low-noise ones.

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Publication info
Published elsewhere. MINOR revision.IEEE Transactions on Very Large Scale Integration (VLSI) Systems ( Volume: 27, Issue: 12, Dec. 2019)
Profiled side-channel attacksFeature selectionMachine learningL1 regularization
Contact author(s)
annelie heuser @ irisa fr
2020-12-07: last of 2 revisions
2017-11-20: received
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      author = {Stjepan Picek and Annelie Heuser and Alan Jovic and Lejla Batina},
      title = {A Systematic Evaluation of Profiling Through Focused Feature Selection},
      howpublished = {Cryptology ePrint Archive, Paper 2017/1110},
      year = {2017},
      note = {\url{}},
      url = {}
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