On the Performance of Convolutional Neural Networks for Side-Channel Analysis

Legay, Axel;Picek, Stjepan;Samiotis, Ioannis Petros;Jaehun, Kim;Bhasin, Shivam;et.al.
(2018) Space 2018 — Location: Indian Institute of Technology, Kanpur (17.December.2018)

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Authors
  • Legay, AxelUCLouvain
    Author
  • Picek, StjepanDelft University of Technology, Mekelweg 2, Delft, The Netherlands
    Author
  • Samiotis, Ioannis PetrosDelft University of Technology, Mekelweg 2, Delft, The Netherlands
    Author
  • Jaehun, KimDelft University of Technology, Mekelweg 2, Delft, The Netherlands
    Author
  • Bhasin, ShivamPhysical Analysis and Cryptographic Engineering, Temasek Laboratories, Nanyang Technological University, Singapore, Singapore
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Abstract
In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated.
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Legay, A., Picek, S., Samiotis, I. P., Jaehun, K., Heuser, A., & Bhasin, S. (2018). On the Performance of Convolutional Neural Networks for Side-Channel Analysis. Security, Privacy, and Applied Cryptography Engineering, pp 157-176. https://doi.org/10.1007/978-3-030-05072-6_10 (Original work published 2018)