对加掩加密算法的盲掩码模板攻击

王燚; 吴震; 蔺冰

doi:10.11959/j.issn.1000-436x.2019007

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对加掩加密算法的盲掩码模板攻击

学术论文 | 更新时间：2024-06-05

- 对加掩加密算法的盲掩码模板攻击
- Blind mask template attacks on masked cryptographic algorithm
- 通信学报 2019年40卷第1期页码：1-14
- 作者机构：
  
  成都信息工程大学网络空间安全学院，四川成都 610225
- 作者简介：
  
  [ "王燚（1968- ），男，四川成都人，博士，成都信息工程大学教授，主要研究方向为机器学习、侧信道攻击与防御、自然语言处理。" ]
  [ "吴震（1975- ），男，江苏苏州人，成都信息工程大学副教授，主要研究方向为信息安全、密码学、侧信道攻击与防御、信息安全设备设计与检测。" ]
  [ "蔺冰（1973- ），男，四川成都人，成都信息工程大学讲师，主要研究方向为信息安全、侧信道攻击与防御、计算机网络。" ]
- 基金信息：
  
  “十三五”国家密码发展基金资助项目(MMJJ20180224);四川省教育厅科研基金资助项目(17ZB0082)
- DOI：10.11959/j.issn.1000-436x.2019007
  中图分类号： TP309.1
- 网络首发：2019-01，
  
  纸质出版：2019-01-25
- 稿件说明：
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王燚, 吴震, 蔺冰. 对加掩加密算法的盲掩码模板攻击[J]. 通信学报, 2019,40(1):1-14.

Yi WANG, Zhen WU, Bing LIN. Blind mask template attacks on masked cryptographic algorithm[J]. Journal on Communications, 2019, 40(1): 1-14.
王燚, 吴震, 蔺冰. 对加掩加密算法的盲掩码模板攻击[J]. 通信学报, 2019,40(1):1-14. DOI： 10.11959/j.issn.1000-436x.2019007.

Yi WANG, Zhen WU, Bing LIN. Blind mask template attacks on masked cryptographic algorithm[J]. Journal on Communications, 2019, 40(1): 1-14. DOI： 10.11959/j.issn.1000-436x.2019007.

摘要

加掩是在加密算法的实现中使用随机掩码使敏感信息的泄露能耗随机化，从而防止差分能量攻击的技术手段。目前，对加掩防护加密算法的模板攻击的方法均要求攻击者在学习阶段了解使用的掩码。这一要求不仅提高了攻击的条件，同时也可能导致模板学习阶段使用的加密代码与实际设备的代码有所不同，进而导致对实际设备攻击效果较差。盲掩码模板攻击不需要了解训练能迹使用的掩码，直接学习无掩中间组合值的模板，以此攻击加掩加密设备。实验中分别采用传统的高斯分布和神经网络建立模板。实验结果证明这种方法是可行的，而且基于神经网络的盲掩码模板攻击对加掩加密设备的攻击成功率非常接近于传统模板攻击对无掩加密设备的攻击成功率。

Abstract

Masking is a countermeasure against differential power analysis (DPA) attacks on cryptographic devices by using random masks to randomize the leaked power of sensitive information.Template attacks (TA) against cryptographic devices with masking countermeasure by far require attackers have knowledge of masks at the profiling phase.This requirement not only increase the prerequisite of template attacking

but also lead to some sort of difference between the experimental encryption codes of the profiling device and the codes of commercial cryptographic devices

which might degrade performance in real world attacking.Blind mask template attack directly learns templates for the combination of no mask intermediate values without the need of knowing the masks of training power traces

and then uses these templates to attack masked cryptographic devices.Both traditional Gaussian distribution and neural network were adopted as the templates in experiments.Experimental results verified the feasibility of this new approach.The success rate of neural network based blind mask template attacking against masked cryptographic devices is very close to that of traditional template attacks against cryptographic devices without masking countermeasure.

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references

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