An Efficient Side Channel Attack Technique with Improved Correlation Power Analysis

Do, Ngoc-Tuan; Hoang, Van‐Phuc

doi:10.1007/978-3-030-63083-6_22

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…In the case of CNN‐based model, we reuse the CNN model as described in Table 3, which is optimised in Ref. [19]. The details of the proposed models are presented in Table 4.…”

Section: Resultsmentioning

confidence: 99%

“…In another work, Alipour et al [10] used a simple method in order to reduce the sampling points of each power trace by 50%. Recently, in our previous work [19], an improved correlation method was presented for taking the most relevant samples in the power trace by computing the correlation between real traces with their model. This method is suitable for a large number of samples because attackers do not need to know detail about the AES implementation.…”

Section: Points Of Interest For Non-profiled Attackmentioning

confidence: 99%

“…According to the authors in Ref. [19], for collecting the useful features from the power traces, half of the power traces are used and denoted as…”

Section: Dataset Reconstructionmentioning

confidence: 99%

“…According to the authors in Ref. [19], for collecting the useful features from the power traces, half of the power traces are used and denoted as

{N}_{1}\left({N}_{1}=\frac{1}{2}N\right)

. Afterwards, the formula () is applied to select the samples of high correlation from all guess keys (Key = [0; 255]).…”

Section: Data Preparationmentioning

confidence: 99%

See 3 more Smart Citations

On the performance of non‐profiled side channel attacks based on deep learning techniques

Hoang

Doan³

et al. 2022

IET Information Security

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In modern embedded systems, security issues including side-channel attacks (SCAs) are becoming of paramount importance since the embedded devices are ubiquitous in many categories of consumer electronics. Recently, deep learning (DL) has been introduced as a new promising approach for profiled and non-profiled SCAs. This paper proposes and evaluates the applications of different DL techniques including the Convolutional Neural Network and the multilayer perceptron models for non-profiled attacks on the AES-128 encryption implementation. Especially, the proposed network is fine-tuned with different number of hidden layers, labelling techniques and activation functions. Along with the designed models, a dataset reconstruction and labelling technique for the proposed model has also been performed for solving the high dimension data and imbalanced dataset problem. As a result, the DL based SCA with our reconstructed dataset for different targets of ASCAD, RISC-V microcontroller, and ChipWhisperer boards has achieved a higher performance of non-profiled attacks. Specifically, necessary investigations to evaluate the efficiency of the proposed techniques against different SCA countermeasures, such as masking and hiding, have been performed. In addition, the effect of the activation function on the proposed DL models was investigated. The experimental results have clarified that the exponential linear unit function is better than the rectified linear unit in fighting against noise generation-based hiding countermeasure.

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“…In the case of CNN‐based model, we reuse the CNN model as described in Table 3, which is optimised in Ref. [19]. The details of the proposed models are presented in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Section: Points Of Interest For Non-profiled Attackmentioning

confidence: 99%

“…According to the authors in Ref. [19], for collecting the useful features from the power traces, half of the power traces are used and denoted as…”

Section: Dataset Reconstructionmentioning

confidence: 99%

“…According to the authors in Ref. [19], for collecting the useful features from the power traces, half of the power traces are used and denoted as

{N}_{1}\left({N}_{1}=\frac{1}{2}N\right)

. Afterwards, the formula () is applied to select the samples of high correlation from all guess keys (Key = [0; 255]).…”

Section: Data Preparationmentioning

confidence: 99%

See 2 more Smart Citations

On the performance of non‐profiled side channel attacks based on deep learning techniques

Hoang

Doan³

et al. 2022

IET Information Security

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show abstract

Performance Analysis of Non-profiled Side Channel Attack Based on Multi-layer Perceptron Using Significant Hamming Weight Labeling

Hoang

Doan³

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Performance Analysis of Deep Learning Based Non-profiled Side Channel Attacks Using Significant Hamming Weight Labeling

Hoang

Doan³

2023

Mobile Netw Appl

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An Efficient Side Channel Attack Technique with Improved Correlation Power Analysis

Cited by 7 publications

References 7 publications

On the performance of non‐profiled side channel attacks based on deep learning techniques

On the performance of non‐profiled side channel attacks based on deep learning techniques

Performance Analysis of Non-profiled Side Channel Attack Based on Multi-layer Perceptron Using Significant Hamming Weight Labeling

Performance Analysis of Deep Learning Based Non-profiled Side Channel Attacks Using Significant Hamming Weight Labeling

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