Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks

2020 ◽  
Author(s):  
Ngoc-Tuan Do ◽  
Van-Phuc Hoang ◽  
Van-Sang Doan
Keyword(s):  
Neural Networks ◽  
Performance Analysis ◽  
Convolutional Neural Networks ◽  
Side Channel ◽  
Side Channel Attacks

scholarly journals Non-Profiled Deep Learning-based Side-Channel attacks with Sensitivity Analysis

2019 ◽  
pp. 107-131 ◽  
Author(s):  
Benjamin Timon
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Deep Learning ◽  
New Method ◽  
Invariance Property ◽  
Side Channel ◽  
Translation Invariance ◽  
Secret Key ◽  
Side Channel Attacks ◽  
Learning Techniques

Deep Learning has recently been introduced as a new alternative to perform Side-Channel analysis [MPP16]. Until now, studies have been focused on applying Deep Learning techniques to perform Profiled Side-Channel attacks where an attacker has a full control of a profiling device and is able to collect a large amount of traces for different key values in order to characterize the device leakage prior to the attack. In this paper we introduce a new method to apply Deep Learning techniques in a Non-Profiled context, where an attacker can only collect a limited number of side-channel traces for a fixed unknown key value from a closed device. We show that by combining key guesses with observations of Deep Learning metrics, it is possible to recover information about the secret key. The main interest of this method is that it is possible to use the power of Deep Learning and Neural Networks in a Non-Profiled scenario. We show that it is possible to exploit the translation-invariance property of Convolutional Neural Networks [CDP17] against de-synchronized traces also during Non-Profiled side-channel attacks. In this case, we show that this method can outperform classic Non-Profiled attacks such as Correlation Power Analysis. We also highlight that it is possible to break masked implementations in black-box, without leakages combination pre-preprocessing and with no assumptions nor knowledge about the masking implementation. To carry the attack, we introduce metrics based on Sensitivity Analysis that can reveal both the secret key value as well as points of interest, such as leakages and masks locations in the traces. The results of our experiments demonstrate the interests of this new method and show that this attack can be performed in practice.

scholarly journals Reinforcement Learning for Hyperparameter Tuning in Deep Learning-based Side-channel Analysis

2021 ◽  
pp. 677-707
Author(s):  
Jorai Rijsdijk ◽  
Lichao Wu ◽  
Guilherme Perin ◽  
Stjepan Picek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Convolutional Neural Networks ◽  
Random Search ◽  
High Price ◽  
Side Channel ◽  
Q Learning ◽  
Reward Functions

Deep learning represents a powerful set of techniques for profiling sidechannel analysis. The results in the last few years show that neural network architectures like multilayer perceptron and convolutional neural networks give strong attack performance where it is possible to break targets protected with various countermeasures. Considering that deep learning techniques commonly have a plethora of hyperparameters to tune, it is clear that such top attack results can come with a high price in preparing the attack. This is especially problematic as the side-channel community commonly uses random search or grid search techniques to look for the best hyperparameters.In this paper, we propose to use reinforcement learning to tune the convolutional neural network hyperparameters. In our framework, we investigate the Q-Learning paradigm and develop two reward functions that use side-channel metrics. We mount an investigation on three commonly used datasets and two leakage models where the results show that reinforcement learning can find convolutional neural networks exhibiting top performance while having small numbers of trainable parameters. We note that our approach is automated and can be easily adapted to different datasets. Several of our newly developed architectures outperform the current state-of-the-art results. Finally, we make our source code publicly available. https://github.com/AISyLab/Reinforcement-Learning-for-SCA

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.

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