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。
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.
作者
王燚
吴震
蔺冰
WANG Yi;WU Zhen;LIN Bing(College of Information Security Engineering, Chengdu University of Information Technology, Chengdu 610225, China)
出处
《通信学报》
EI
CSCD
北大核心
2019年第1期1-14,共14页
Journal on Communications
基金
"十三五"国家密码发展基金资助项目(No.MMJJ20180224)
国家重点研发计划基金资助项目(No.2018YFB0904900
No.2018YFB0904901)
四川省教育厅科研基金资助项目(No.17ZB0082)~~
关键词
侧信道攻击
模板攻击
盲掩码攻击
加掩防护
神经网络
side channel attack
template attack
blind mask template attack
masking countermeasure
neural network
