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

Authors:
Shuguo Yang;Yongbin Zhou;Jiye Liu;Danyang Chen
Affiliations:
State Key Laboratory of Information Security, Institute of Software Chinese Academy of Sciences, Beijing, P.R. China,Graduate University of Chinese Academy of Sciences, Beijing, P.R. China;State Key Laboratory of Information Security, Institute of Software Chinese Academy of Sciences, Beijing, P.R. China;State Key Laboratory of Information Security, Institute of Software Chinese Academy of Sciences, Beijing, P.R. China,Graduate University of Chinese Academy of Sciences, Beijing, P.R. China;State Key Laboratory of Information Security, Institute of Software Chinese Academy of Sciences, Beijing, P.R. China,School of Mathematics Sciences, Beijing Normal University, Beijing, P.R. China
Venue:
ICISC'11 Proceedings of the 14th international conference on Information Security and Cryptology
Year:
2011

Citing 12
Cited 0

Differential Power Analysis

CRYPTO '99 Proceedings of the 19th Annual International Cryptology Conference on Advances in Cryptology
The EM Side-Channel(s)

CHES '02 Revised Papers from the 4th International Workshop on Cryptographic Hardware and Embedded Systems
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security)

Power Analysis Attacks: Revealing the Secrets of Smart Cards (Advances in Information Security)
Mutual Information Analysis

CHES '08 Proceeding sof the 10th international workshop on Cryptographic Hardware and Embedded Systems
Partition vs. Comparison Side-Channel Distinguishers: An Empirical Evaluation of Statistical Tests for Univariate Side-Channel Attacks against Two Unprotected CMOS Devices

Information Security and Cryptology --- ICISC 2008
Theoretical and Practical Aspects of Mutual Information Based Side Channel Analysis

ACNS '09 Proceedings of the 7th International Conference on Applied Cryptography and Network Security
Mutual Information Analysis: How, When and Why?

CHES '09 Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems
A formal study of power variability issues and side-channel attacks for nanoscale devices

EUROCRYPT'11 Proceedings of the 30th Annual international conference on Theory and applications of cryptographic techniques: advances in cryptology
How to characterize side-channel leakages more accurately?

ISPEC'11 Proceedings of the 7th international conference on Information security practice and experience
A proposition for correlation power analysis enhancement

CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
A stochastic model for differential side channel cryptanalysis

CHES'05 Proceedings of the 7th international conference on Cryptographic hardware and embedded systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.