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

Profiled side-channel analysis 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 able to reach high performance for a number of considered datasets. We compare our neural network with the one designed for a particular dataset with masking countermeasure and we show that both are good designs but also that 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 reduce the number of measurements needed to reveal the secret key by orders of magnitude for both neural networks. Our new convolutional neural network instance with added noise is able to break the implementation protected with the random delay countermeasure by using only 3 traces in the attack phase. To further strengthen our experimental results, we investigate the performance with a varying number of training samples, noise levels, and epochs. Our findings show that adding noise is beneficial throughout all training set sizes and epochs.

[1]  Sylvain Guilley,et al.  Good is Not Good Enough: Deriving Optimal Distinguishers from Communication Theory , 2014, IACR Cryptol. ePrint Arch..

[2]  Emmanuel Prouff,et al.  Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures - Profiling Attacks Without Pre-processing , 2017, CHES.

[3]  Dawu Gu,et al.  Trace Augmentation: What Can Be Done Even Before Preprocessing in a Profiled SCA? , 2017, CARDIS.

[4]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[5]  Sylvain Guilley,et al.  Detecting Hidden Leakages , 2014, ACNS.

[6]  Olivier Markowitch,et al.  A machine learning approach against a masked AES , 2014, Journal of Cryptographic Engineering.

[7]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  Sylvain Guilley,et al.  Template attack versus Bayes classifier , 2017, Journal of Cryptographic Engineering.

[10]  Annelie Heuser,et al.  The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations , 2018, IACR Cryptol. ePrint Arch..

[11]  Juhan Nam,et al.  Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[12]  Olivier Markowitch,et al.  Power analysis attack: an approach based on machine learning , 2014, Int. J. Appl. Cryptogr..

[13]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[14]  Axel Legay,et al.  Climbing Down the Hierarchy: Hierarchical Classification for Machine Learning Side-Channel Attacks , 2017, AFRICACRYPT.

[15]  Joos Vandewalle,et al.  Machine learning in side-channel analysis: a first study , 2011, Journal of Cryptographic Engineering.

[16]  Christopher M. Bishop,et al.  Current address: Microsoft Research, , 2022 .

[17]  Markus G. Kuhn,et al.  Efficient Template Attacks , 2013, CARDIS.

[18]  Sylvain Guilley,et al.  Side-channel analysis and machine learning: A practical perspective , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[19]  Pascal Vincent,et al.  Adding noise to the input of a model trained with a regularized objective , 2011, ArXiv.

[20]  Moti Yung,et al.  A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks (extended version) , 2009, IACR Cryptol. ePrint Arch..

[21]  Jean-Sébastien Coron,et al.  An Efficient Method for Random Delay Generation in Embedded Software , 2009, CHES.

[22]  Christof Paar,et al.  A Stochastic Model for Differential Side Channel Cryptanalysis , 2005, CHES.

[23]  David H. Wolpert,et al.  The Lack of A Priori Distinctions Between Learning Algorithms , 1996, Neural Computation.

[24]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[25]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[26]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[27]  Cécile Canovas,et al.  Study of Deep Learning Techniques for Side-Channel Analysis and Introduction to ASCAD Database , 2018, IACR Cryptol. ePrint Arch..

[28]  Axel Legay,et al.  On the Performance of Convolutional Neural Networks for Side-Channel Analysis , 2018, SPACE.

[29]  Annelie Heuser,et al.  Intelligent Machine Homicide - Breaking Cryptographic Devices Using Support Vector Machines , 2012, COSADE.

[30]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[31]  Richard Simon,et al.  Resampling Strategies for Model Assessment and Selection , 2007 .

[32]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[33]  Emmanuel Prouff,et al.  Breaking Cryptographic Implementations Using Deep Learning Techniques , 2016, SPACE.

[34]  Luca Antiga,et al.  Automatic differentiation in PyTorch , 2017 .

[35]  Máire O'Neill,et al.  Neural network based attack on a masked implementation of AES , 2015, 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST).

[36]  Christophe Clavier,et al.  Correlation Power Analysis with a Leakage Model , 2004, CHES.

[37]  Sylvain Guilley,et al.  Lightweight Ciphers and Their Side-Channel Resilience , 2020, IEEE Transactions on Computers.

[38]  Romain Poussier,et al.  Template Attacks vs. Machine Learning Revisited (and the Curse of Dimensionality in Side-Channel Analysis) , 2015, COSADE.