A novel non-profiled side channel attack based on multi-output regression neural network

Do, Ngoc-Tuan; Hoang, Van‐Phuc; Doan, Van‐Sang

doi:10.1007/s13389-023-00314-4

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…When facing ASCAD, all methods perform well. In the case of ASCAD_desync50, NASCA-CNN provides a higher success rate than DDLA-CNN in [16] and MOR-CNN4 in [23]. When Gaussian noise is introduced to ASCAD, although the performance of all methods experiences a decline, NASCA-CNN still has a slight advantage.…”

Section: Performance Evaluationmentioning

confidence: 98%

“…However, our proposed models other existing works in terms of trace consumption against mask countermeasures. [16] 1200 low 20,000 DDLA-CNN [16] 11030 low 20,000 MOR-CNN4 [23] 270 low 40,000 MOR-MLP3 [23] 700 moderate 40,000 ACNN [18] high 100,000…”

Section: Performance Evaluationmentioning

confidence: 99%

“…In addition, most existing works follow the metrics proposed by Timon for key recovery [22][23][24][25]. However, these metrics suffer from a drawback, as they lack quantitative analysis and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism

Pu,

Dang,

Kong

et al. 2023

Electronics

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In recent years, the deep learning method has emerged as a mainstream approach to non-profiled side-channel attacks. However, most existing methods of deep learning-based non-profiled side-channel attack rely on traditional metrics such as loss and accuracy, which often suffer from unclear results in practical scenarios. Furthermore, most previous studies have not fully considered the properties of power traces as long time-series data. In this paper, a novel non-profiled side-channel attack architecture is proposed, which incorporates the attention mechanism and derives a corresponding attention metric. By attaching the attention mechanism after the network layers, the attention mechanism provides a quantitative prediction of correct key. Moreover, this architecture can effectively extract and analyze the features from long power traces. The success rate on different datasets is at least 86%, which demonstrates the superior reliability of this architecture compared to other works when facing various countermeasures and noise. Notably, even in scenarios where traditional loss and accuracy metrics fail to provide reliable results, the proposed attention metric remains capable of accurately distinguishing the correct key.

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Section: Performance Evaluationmentioning

confidence: 98%

Section: Performance Evaluationmentioning

confidence: 99%

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A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism

Pu,

Dang,

Kong

et al. 2023

Electronics

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“…«Ветви» могут интерпретироваться как отдельные полносвязные ИНС с отдельным выходом (рисунок 3). Подобные ИНС получили наименование «ИНС с множественным выходом» (многозначным выходом) [27,28].…”

Section: многозначная классификация с помощью инс с множественным вых...unclassified

Multivalued Classification of Computer Attacks Using Artificial Neural Networks with Multiple Outputs

Shelukhin,

Rakovsky

2023

jour

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Modern computer networks (CN), having a complex and often heterogeneous structure, generate large volumes of multi-dimensional multi-label data. Accounting for information about multi-label experimental data (ED) can improve the efficiency of solving a number of information security problems: from CN profiling to detecting and preventing computer attacks on CN. The aim of the work is to develop a multi-label artificial neural network (ANN) architecture for detecting and classifying computer attacks in multi-label ED, and its comparative analysis with known analogues in terms of binary metrics for assessing the quality of classification. A formalization of ANN in terms of matrix algebra is proposed, which allows taking into account the case of multi-label classification and the new architecture of ANN with multiple output using the proposed formalization. The advantage of the proposed formalization is the conciseness of a number of entries associated with the ANN operating mode and learning mode. Proposed architecture allows solving the problems of detecting and classifying multi-label computer attacks, on average, 5% more efficiently than known analogues. The observed gain is due to taking into account multi-label patterns between class labels at the training stage through the use of a common first layer. The advantages of the proposed ANN architecture are scalability to any number of class labels and fast convergence.

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The Need for MORE: Unsupervised Side-Channel Analysis with Single Network Training and Multi-output Regression

Savu,

Krček,

Perin

et al. 2024

Lecture Notes in Computer Science

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A novel non-profiled side channel attack based on multi-output regression neural network

Cited by 7 publications

References 26 publications

A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism

A Quantitative Analysis of Non-Profiled Side-Channel Attacks Based on Attention Mechanism

Multivalued Classification of Computer Attacks Using Artificial Neural Networks with Multiple Outputs

The Need for MORE: Unsupervised Side-Channel Analysis with Single Network Training and Multi-output Regression

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