Paper 2018/1023

Make Some Noise: Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis

Jaehun Kim, Stjepan Picek, Annelie Heuser, Shivam Bhasin, and Alan Hanjalic


Profiled side-channel attacks based on deep learning, and more precisely Convolutional Neural Networks, is a paradigm showing significant potential. The results, although scarce for now, suggest that such techniques are even able to break cryptographic implementations protected with countermeasures. In this paper, we start by proposing a new Convolutional Neural Network instance that is able to reach high performance for a number of considered datasets. Additionally, for a dataset protected with the random delay countermeasure, our neural network is able to break the implementation by using only 2 traces in the attack phase. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show how both are good designs but also how neither can be considered as a superior to the other one. Next, we address how the addition of artificial noise to the input signal can be actually beneficial to the performance of the neural network. Such noise addition is equivalent to the regularization term in the objective function. By using this technique, we are able to improve the number of measurement needed to reveal the secret key by orders of magnitude in certain scenarios for both neural networks. To strengthen our experimental results, we experiment with a number of datasets which differ in the levels of noise (and type of countermeasure) where we show the viability of our approaches.

Available format(s)
Publication info
Side-channel analysisConvolutional Neural NetworksMachine learningGaussian noise
Contact author(s)
picek stjepan @ gmail com
2018-10-26: received
Short URL
Creative Commons Attribution


      author = {Jaehun Kim and Stjepan Picek and Annelie Heuser and Shivam Bhasin and Alan Hanjalic},
      title = {Make Some Noise: Unleashing the Power of Convolutional Neural Networks for Profiled Side-channel Analysis},
      howpublished = {Cryptology ePrint Archive, Paper 2018/1023},
      year = {2018},
      note = {\url{}},
      url = {}
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