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

2018 ◽  
pp. 157-176 ◽  
Author(s):  
Stjepan Picek ◽  
Ioannis Petros Samiotis ◽  
Jaehun Kim ◽  
Annelie Heuser ◽  
Shivam Bhasin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

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

2019 ◽  
pp. 148-179 ◽  
Author(s):  
Jaehun Kim ◽  
Stjepan Picek ◽  
Annelie Heuser ◽  
Shivam Bhasin ◽  
Alan Hanjalic
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
High Performance ◽  
Artificial Noise ◽  
Side Channel ◽  
Secret Key ◽  
Side Channel Analysis ◽  
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.

scholarly journals Convolutional Neural Networks for Profiled Side-channel Analysis

2019 ◽  
Vol 27 (3) ◽  
pp. 651-658
Author(s):  
S. R. Hou ◽  
Y. J. Zhou ◽  
H. M. Liu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Side Channel ◽  
Side Channel Analysis ◽  
Channel Analysis

scholarly journals Plaintext: A Missing Feature for Enhancing the Power of Deep Learning in Side-Channel Analysis?

2020 ◽  
pp. 49-85
Author(s):  
Anh-Tuan Hoang ◽  
Neil Hanley ◽  
Maire O’Neill
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Body ◽  
Side Channel ◽  
Secret Key ◽  
Side Channel Analysis ◽  
Filter Kernel ◽  
Secret Keys ◽  
Channel Analysis ◽  
Template Attacks

Deep learning (DL) has proven to be very effective for image recognition tasks, with a large body of research on various model architectures for object classification. Straight-forward application of DL to side-channel analysis (SCA) has already shown promising success, with experimentation on open-source variable key datasets showing that secret keys can be revealed with 100s traces even in the presence of countermeasures. This paper aims to further improve the application of DL for SCA, by enhancing the power of DL when targeting the secret key of cryptographic algorithms when protected with SCA countermeasures. We propose a new model, CNN-based model with Plaintext feature extension (CNNP) together with multiple convolutional filter kernel sizes and structures with deeper and narrower neural networks, which has empirically proven its effectiveness by outperforming reference profiling attack methods such as template attacks (TAs), convolutional neural networks (CNNs) and multilayer perceptron (MLP) models. Our model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 40 attack traces in comparison with order of 100s traces required by straightforward machine learning (ML) application. During the profiling stage an attacker needs no additional knowledge on the implementation, such as the masking scheme or random mask values, only the ability to record the power consumption or electromagnetic field traces, plaintext/ciphertext and the key. Additionally, no heuristic pre-processing is required in order to break the high-order masking countermeasures of the target implementation.

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