Optimizing label correlation in deep learning-based side-channel analysis

IF 1.9 3区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

Microelectronics Journal Pub Date : 2025-05-23 DOI:10.1016/j.mejo.2025.106721

Shengcheng Xia, Lang Li, Yu Ou, Jiahao Xiang

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引用次数: 0

Abstract

Label distribution learning techniques can significantly enhance the effectiveness of side-channel analysis. However, this method relies on using probability density functions to estimate the relationships between labels. The settings of parameters play a crucial role in the impact of the attacks. This study introduces a non-parametric statistical method to calculate the distribution between labels, specifically employing smoothing with the Gaussian kernel function and adjusting bandwidth. Then, the aggregation of the results from each label processed by the Gaussian kernel facilitates a hypothesis-free estimation of the label distribution. This method accurately represents the actual leakage distribution, speeding up guess entropy convergence. Secondly, we exploit similarities between profiling traces, proposing an analysis scheme for sample correlation locally of label distribution learning. Furthermore, Signal to-Noise Ratio (SNR) is employed to re-extract and reduce dataset dimensions to 500 power consumption points, resulting in noise reduction. Our results showcase the successful training of 800 profiling traces using our method for sample correlation locally of label distribution learning, with the findings indicating its exceptional performance.

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基于深度学习的边信道分析中标签相关性的优化

标签分布学习技术可以显著提高侧信道分析的有效性。然而，该方法依赖于使用概率密度函数来估计标签之间的关系。参数的设置对攻击的影响起着至关重要的作用。本文引入了一种非参数统计方法来计算标签之间的分布，具体采用高斯核函数平滑和调整带宽。然后，由高斯核处理的每个标签结果的聚合有助于对标签分布进行无假设估计。该方法准确地反映了实际泄漏分布，加快了猜测熵的收敛速度。其次，我们利用分析轨迹之间的相似性，提出了一种局部标签分布学习的样本相关性分析方案。进一步，采用信噪比（SNR）对数据集进行重新提取并降维至500个功耗点，实现降噪。我们的研究结果显示，使用我们的方法在标签分布学习的局部样本相关性成功训练了800个分析痕迹，结果表明它具有出色的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Microelectronics Journal 工程技术-工程：电子与电气

CiteScore

4.00

自引率

27.30%

发文量

222

审稿时长

43 days

期刊介绍： Published since 1969, the Microelectronics Journal is an international forum for the dissemination of research and applications of microelectronic systems, circuits, and emerging technologies. Papers published in the Microelectronics Journal have undergone peer review to ensure originality, relevance, and timeliness. The journal thus provides a worldwide, regular, and comprehensive update on microelectronic circuits and systems. The Microelectronics Journal invites papers describing significant research and applications in all of the areas listed below. Comprehensive review/survey papers covering recent developments will also be considered. The Microelectronics Journal covers circuits and systems. This topic includes but is not limited to: Analog, digital, mixed, and RF circuits and related design methodologies; Logic, architectural, and system level synthesis; Testing, design for testability, built-in self-test; Area, power, and thermal analysis and design; Mixed-domain simulation and design; Embedded systems; Non-von Neumann computing and related technologies and circuits; Design and test of high complexity systems integration; SoC, NoC, SIP, and NIP design and test; 3-D integration design and analysis; Emerging device technologies and circuits, such as FinFETs, SETs, spintronics, SFQ, MTJ, etc. Application aspects such as signal and image processing including circuits for cryptography, sensors, and actuators including sensor networks, reliability and quality issues, and economic models are also welcome.