Paper 2013/865

SNR to Success Rate: Reaching the Limit of Non-Profiling DPA

Suvadeep Hajra and Debdeep Mukhopadhyay

Abstract

Profiling power attacks like Template attack and Stochastic attack optimizes their performance by jointly evaluating the leakages of multiple sample points. However, such multivariate approaches are rare among non-profiling DPA attacks, since integration of the leakage of a higher Signal-to-Noise Ratio (SNR) sample point with the leakages of lower SNR sample points might result in a decrease in the overall performance. We study the issue of optimally combining the leakages of multiple sample points using a linear function in great details. In this work, our contributions are three-fold: 1) we first derive a relation between the success rate of a CPA attack and the SNR of the power traces, 2) we introduce a multivariate leakage model for Virtex-5 FPGA device, and 3) using the proposed multivariate leakage model, we derive the linear Finite Impulse Response (FIR) filter coefficients which maximizes the SNR of the output leakage, thus optimizes the success rate of the CPA attacks in a non-profiling setup.