back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations

ISCAS OpenIR

	back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations
	Yang Shuguo; Zhou Yongbin; Liu Jiye; Chen Danyang
	2012
会议名称	14th International Conference on Information Security and Cryptology, ICISC 2011
会议录名称	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
页码	169-185
会议日期	November 30, 2011 - December 2, 2011
会议地点	Seoul, Korea, Republic of
收录类别	EI
ISSN	0302-9743
ISBN	9783642319112
部门归属	(1) State Key Laboratory of Information Security Institute of Software Chinese Academy of Sciences P.O. Box 8718 Beijing 100190 China; (2) Graduate University of Chinese Academy of Sciences 19A Yuquan Lu Beijing 100049 China; (3) School of Mathematics Sciences Beijing Normal University No.19 XinJieKouWai St. Beijing 100875 China
摘要	Side-channel attacks have posed serious threats to the physical security of cryptographic implementations. However, the effectiveness of these attacks strongly depends on the accuracy of underlying side-channel leakage characterization. Known leakage characterization models do not always apply into the real scenarios as they are working on some unrealistic assumptions about the leaking devices. In light of this, we propose a back propagation neural network based power leakage characterization attack for cryptographic devices. This attack makes full use of the intrinsic advantage of neural network in profiling non-linear mapping relationship as one basic machine learning tool, transforms the task of leakage profiling into a neural-network-supervised study process. In addition, two new attacks using this model have also been proposed, namely BP-CPA and BP-MIA. In order to justify the validity and accuracy of proposed attacks, we perform a series of experiments and carry out a detailed comparative study of them in multiple scenarios, with twelve typical attacks using mainstream power leakage characterization attacks, the results of which are measured by quantitative metrics such as SR, GE and DL. It has been turned out that BP neural network based power leakage characterization attack can largely improve the effectiveness of the attacks, regardless of the impact of noise and the limited number of power traces. Taking CPA only as one example, BP-CPA is 16.5% better than existing non-linear leakage characterized based attacks with respect to DL, and is 154% better than original CPA. © 2012 Springer-Verlag.; Side-channel attacks have posed serious threats to the physical security of cryptographic implementations. However, the effectiveness of these attacks strongly depends on the accuracy of underlying side-channel leakage characterization. Known leakage characterization models do not always apply into the real scenarios as they are working on some unrealistic assumptions about the leaking devices. In light of this, we propose a back propagation neural network based power leakage characterization attack for cryptographic devices. This attack makes full use of the intrinsic advantage of neural network in profiling non-linear mapping relationship as one basic machine learning tool, transforms the task of leakage profiling into a neural-network-supervised study process. In addition, two new attacks using this model have also been proposed, namely BP-CPA and BP-MIA. In order to justify the validity and accuracy of proposed attacks, we perform a series of experiments and carry out a detailed comparative study of them in multiple scenarios, with twelve typical attacks using mainstream power leakage characterization attacks, the results of which are measured by quantitative metrics such as SR, GE and DL. It has been turned out that BP neural network based power leakage characterization attack can largely improve the effectiveness of the attacks, regardless of the impact of noise and the limited number of power traces. Taking CPA only as one example, BP-CPA is 16.5% better than existing non-linear leakage characterized based attacks with respect to DL, and is 154% better than original CPA. © 2012 Springer-Verlag.
关键词	Cryptography Neural Networks Security Of Data
主办者	National Security Research Institute (NSRI); Electronics and Telecommunications Research Institute (ETRI); Korea Internet and Security Agency (KISA); Ministry of Public Administration and Security (MOPAS)
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15743
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Yang Shuguo,Zhou Yongbin,Liu Jiye,et al. back propagation neural network based leakage characterization for practical security analysis of cryptographic implementations[C],2012:169-185.