• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE Asia-Pacific Conference on Circuits and Systems >Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks
【24h】

Performance Analysis of Non-Profiled Side Channel Attacks Based on Convolutional Neural Networks

机译：基于卷积神经网络的非分布侧信道攻击性能分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There are emerging issues about side channel at-tacks (SCAs) on the cryptographic devices which are widely used today for securing secret information. Recently, the neural networks have been introduced as a new promising approach to perform SCA for hardware security evaluation of cryptographic algorithms. In this work, we present a non-profiled SCA using convolutional neural networks (CNNs) on an 8-bit AVR micro-controller device running the AES-128 cryptographic algorithm. We aim to point out the practical issues that occurs in CNN based SCA methods using the aligned power traces with a large number of samples. Furthermore, a method to build a suitable dataset for CNN training is introduced. Especially, practical experiment results of the CNN based SCA methods and a comprehensive investigation on the effect of noise are also presented. These experiments are performed with the original power traces and additive Gaussian noise. The results show that the CNN based SCA with our constructed dataset provides reliable results for non-profiled attacks. However, it is also shown that the Gaussian noise added on power traces becomes a serious problem.
机译：在今天广泛使用的加密设备上有关于侧通道（SCAS）的侧通道（SCAS）的出现问题，以确保秘密信息。最近，神经网络被引入作为对Cryptogation算法的硬件安全评估执行SCA的新有希望的方法。在这项工作中，我们在运行AES-128加密算法的8位AVR微控制器设备上使用卷积神经网络（CNNS）提供非分布的SCA。我们的目标是指出，使用具有大量样品的对齐的电源迹线，指出基于CNN的SCA方法的实际问题。此外，介绍了构建用于CNN训练的合适数据集的方法。特别是，还介绍了基于CNN的CNN的实际实验结果和对噪声效果的综合调查。这些实验是用原始电力迹线和添加剂高斯噪声进行的。结果表明，基于CNN的SCA与我们构造的数据集提供了非分类攻击的可靠结果。然而，还表明，在电力迹线上添加的高斯噪声成为一个严重的问题。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号