Pushing the Limit of Non-Profiling DPA using Multivariate Leakage Model

Profiling power attacks like Template attack and Stochastic attack optimize their performance by jointly evaluating the leakages of multiple sample points. However, such multivariate approaches are rare among non-profiling Differential Power Analysis (DPA) attacks, since integration of the leakage of a higher SNR sample point with the leakage of lower SNR sample point might result in a decrease in the overall performance. One of the few successful multivariate approaches is the application of Principal Component Analysis (PCA) for non-profiling DPA. However, PCA also performs sub-optimally in the presence of high noise. In this paper, a multivariate model for an FPGA platform is introduced for improving the performances of nonprofiling DPA attacks. The introduction of the proposed model greatly increases the success rate of DPA attacks in the presence of high noise. The experimental results on both simulated power traces and real power traces are also provided as an evidence.