Profiled Power Analysis Attacks Using Convolutional Neural Networks with Domain Knowledge

Hettwer, Benjamin; Gehrer, Stefan; Güneysu, Tim

doi:10.1007/978-3-030-10970-7_22

Cited by 26 publications

(25 citation statements)

References 22 publications

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“…With the help of deep learning techniques, we might able to choose from a wider range of leakage sources, such as, electromagnetic leakage 30 and domain knowledge. 28 We hope that this paper will inspire more related work.…”

Section: Resultsmentioning

confidence: 89%

“…Finally, we have demonstrated how to combine the static power and dynamic power leakage in this work, but the possible leakage sources to be considered should not be limited to these. With the help of deep learning techniques, we might able to choose from a wider range of leakage sources, such as, electromagnetic leakage and domain knowledge . We hope that this paper will inspire more related work.…”

Section: Resultsmentioning

confidence: 99%

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Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Heys

2019

Circuit Theory & Apps

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Summary Side‐channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side‐channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side‐channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short‐term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45‐nm library.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Heys

2019

Circuit Theory & Apps

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“…Their experiments were based on the assumption that this additional information can be used to improve the learning performance. The results in showed that the adoption of the DK neurons improved the performance. In addition, they observed that learning the round key outperforms the case of learning the output of the Sbox.…”

Section: Deep Learning‐based Side‐channel Analysismentioning

confidence: 95%

“…Note that CNN is composed of two basic parts: feature extraction and feature classification/regression based on the extracted feature. Hettwer and others proposed a CNN architecture with domain knowledge (DK) neurons . DK neurons are used as an additional input to the fully connected layer—as additional inputs for feature classification/regression.…”

Section: Deep Learning‐based Side‐channel Analysismentioning

confidence: 99%

Recent advances in deep learning‐based side‐channel analysis

Jin

Kim

et al. 2020

ETRI Journal

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As side‐channel analysis and machine learning algorithms share the same objective of classifying data, numerous studies have been proposed for adapting machine learning to side‐channel analysis. However, a drawback of machine learning algorithms is that their performance depends on human engineering. Therefore, recent studies in the field focus on exploiting deep learning algorithms, which can extract features automatically from data. In this study, we survey recent advances in deep learning‐based side‐channel analysis. In particular, we outline how deep learning is applied to side‐channel analysis, based on deep learning architectures and application methods. Furthermore, we describe its properties when using different architectures and application methods. Finally, we discuss our perspective on future research directions in this field.

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“…A state of the art effort in including SCA domain knowledge into DL architectures was given by [HGG18], in which the plaintext was given as an additional input to increase the accuracy when directly training on the key value. While this paper shares a similar approach to input domain knowledge, there is a significant difference with regards to architecture aspects such as the length of PoI and network structure, affecting how the domain knowledge and PoI leakage spread through the network layers, enabling our model to work against cryptographic implementations with complex high-order countermeasures.…”

Section: Related Workmentioning

confidence: 99%

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

Hoang

Hanley

O’Neill

2020

TCHES

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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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Profiled Power Analysis Attacks Using Convolutional Neural Networks with Domain Knowledge

Cited by 26 publications

References 22 publications

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Recent advances in deep learning‐based side‐channel analysis

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

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