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

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	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
会议日期	November 30, 2011 - December 2, 2011
会议地点	Seoul, Korea, Republic of
关键词	Cryptography Neural networks Security of data
页码	169-185
中文摘要	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.
英文摘要	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.
收录类别	EI
会议主办者	National Security Research Institute (NSRI); Electronics and Telecommunications Research Institute (ETRI); Korea Internet and Security Agency (KISA); Ministry of Public Administration and Security (MOPAS)
会议录	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
语种	英语
ISSN号	0302-9743
ISBN号	9783642319112
内容类型	会议论文
源URL	[http://ir.iscas.ac.cn/handle/311060/15743]
专题	软件研究所_软件所图书馆_会议论文
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]. 见:14th International Conference on Information Security and Cryptology, ICISC 2011. Seoul, Korea, Republic of. November 30, 2011 - December 2, 2011.