基于半监督学习的AES算法功耗分析
基于机器学习的功耗分析是目前功耗分析的主要研究方向之一, 属于建模类的攻击. 针对无掩码防护的AES算法实现, 本文将半监督机器学习算法 Tri-Training应用于功耗分析, 有效减少了用机器学习算法进行建模时所需要的有标记能量迹数量. 相较于基于有监督机器学习的建模类功耗分析, 使用Tri-Training算法可以有效减小对有标记能量迹的需求, 更具有现实意义. 然而, Tri-Training算法在初始分类器较弱时, 容易出现错误标记现象, 影响分类的准确率和建模的效率. 对此本文在使用Tri-Training算法进行建模时引入了阈值判断操作, 提高了分类的准确率, 并对比了不同阈值对...
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| Published in | Journal of Cryptologic Research Vol. 8; no. 4; p. 660 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
Beijing
Chinese Association for Cryptologic Research, Journal of Cryptologic Research
01.01.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2097-4116 |
| DOI | 10.13868/j.cnki.jcr.000467 |
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| Abstract | 基于机器学习的功耗分析是目前功耗分析的主要研究方向之一, 属于建模类的攻击. 针对无掩码防护的AES算法实现, 本文将半监督机器学习算法 Tri-Training应用于功耗分析, 有效减少了用机器学习算法进行建模时所需要的有标记能量迹数量. 相较于基于有监督机器学习的建模类功耗分析, 使用Tri-Training算法可以有效减小对有标记能量迹的需求, 更具有现实意义. 然而, Tri-Training算法在初始分类器较弱时, 容易出现错误标记现象, 影响分类的准确率和建模的效率. 对此本文在使用Tri-Training算法进行建模时引入了阈值判断操作, 提高了分类的准确率, 并对比了不同阈值对分类准确率的影响. 本文对在ATM89S52单片机上实现的AES-128算法进行建模类功耗分析, 实验结果表明, 在使用80条有标记能量迹时, 相较于使用有监督学习算法的准确率为63.49%, 本方法的准确率为74.56%, 准确率提升了约11%. |
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| AbstractList | 基于机器学习的功耗分析是目前功耗分析的主要研究方向之一, 属于建模类的攻击. 针对无掩码防护的AES算法实现, 本文将半监督机器学习算法 Tri-Training应用于功耗分析, 有效减少了用机器学习算法进行建模时所需要的有标记能量迹数量. 相较于基于有监督机器学习的建模类功耗分析, 使用Tri-Training算法可以有效减小对有标记能量迹的需求, 更具有现实意义. 然而, Tri-Training算法在初始分类器较弱时, 容易出现错误标记现象, 影响分类的准确率和建模的效率. 对此本文在使用Tri-Training算法进行建模时引入了阈值判断操作, 提高了分类的准确率, 并对比了不同阈值对分类准确率的影响. 本文对在ATM89S52单片机上实现的AES-128算法进行建模类功耗分析, 实验结果表明, 在使用80条有标记能量迹时, 相较于使用有监督学习算法的准确率为63.49%, 本方法的准确率为74.56%, 准确率提升了约11%. |
| Author | Yong-Juan, WANG 王相宾 高光普 Xiang-Bin, WANG ZHAO, Yuan 赵远 Qing-Jun, YUAN 袁庆军 王永娟 GAO Guang-Pu |
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| Title | 基于半监督学习的AES算法功耗分析 |
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