检索结果-南通市图书馆

Non-profiled Deep-Learning-Based Power Analysis of the SM4 and DES algorithms

Chinese Journal of Electronics 2021年第3期30卷 500-507页

作者： MA Xiangliang LI Bing WANG Hong WU Di ZHANG Lizhen HUANG Kezhen DUAN Xiaoyi School of Integrated Circuits Tsinghua University National Research Center for Information Technology Security China Cybersecurity Review Technology and Certification Center Trusted Computing and Information Assurance Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Department of Electronics and Information Engineering Beijing Electronic Science and Technology Institute

Power analysis methods are commonly used for evaluating the security of cryptographic *** are characteristically low-cost and display a high success rate and the ability to obtain important device information, e.g., keys. Given the current wide application of deep-learning technology, there is a growing tendency to incorporate power-analysis technology in *** study investigates non-profiled deep-learning-based power analysis. The labels used in this attack are uncertain,and the attack conditions required are greatly reduced. We choose the Recurrent neural network(rnn), multilayer perceptron, and convolutional neural network algorithms,which use the same network structure, to recover the keys for the SM4 software and DES hardware *** propose combining the rnn algorithm with power analysis, and validate the benefits experimentally. The experimental results show that they all successfully recover the correct key for the SM4 software implementation,although the rnn algorithm by itself achieves a better effect. This conclusion also applies to attacks on the DES hardware implementation but is limited to labels based on the bit model.

关键词： cryptography nonprofiled deep-learning-based power analysis cryptographic device security recurrent neural nets rnn algorithm deep learning (artificial intelligence) DES hardware implementation convolutional neural nets convolutional neural network algorithms SM4 software implementation multilayer perceptrons recurrent neural network multilayer perceptron

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：