Pairwise classification and support vector machines
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
CHES '02 Revised Papers from the 4th International Workshop on Cryptographic Hardware and Embedded Systems
Performance Modeling of Analog Integrated Circuits Using Least-Squares Support Vector Machines
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Template attacks in principal subspaces
CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
Templates vs. stochastic methods
CHES'06 Proceedings of the 8th international conference on Cryptographic Hardware and Embedded Systems
WISA'04 Proceedings of the 5th international conference on Information Security Applications
A stochastic model for differential side channel cryptanalysis
CHES'05 Proceedings of the 7th international conference on Cryptographic hardware and embedded systems
Analyzing side channel leakage of masked implementations with stochastic methods
ESORICS'07 Proceedings of the 12th European conference on Research in Computer Security
Efficient template attacks based on probabilistic multi-class support vector machines
CARDIS'12 Proceedings of the 11th international conference on Smart Card Research and Advanced Applications
Practical template-algebraic side channel attacks with extremely low data complexity
Proceedings of the 2nd International Workshop on Hardware and Architectural Support for Security and Privacy
Semi-Supervised template attack
COSADE'13 Proceedings of the 4th international conference on Constructive Side-Channel Analysis and Secure Design
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In this contribution we propose the so-called SVM attack, a profiling based side channel attack, which uses the machine learning algorithm support vector machines (SVM) in order to recover a cryptographic secret. We compare the SVM attack to the template attack by evaluating the number of required traces in the attack phase to achieve a fixed guessing entropy. In order to highlight the benefits of the SVM attack, we perform the comparison for power traces with a varying noise level and vary the size of the profiling base. Our experiments indicate that due to the generalization of SVM the SVM attack is able to recover the key using a smaller profiling base than the template attack. Thus, the SVM attack counters the main drawback of the template attack, i.e. a huge profiling base.