Breaking Masked and Shuffled CCA Secure Saber KEM by Power Analysis

Abstract

In this paper, we show that a software implementation of CCA secure Saber KEM protected by first-order masking and shuffling can be broken by deep learning-based power analysis. Using an ensemble of deep neural networks created at the profiling stage, we can recover the session key and the long-term secret key from 257xN and 24x257xN traces, respectively, where N is the number of repetitions of the same measurement. The value of N depends on the implementation, environmental factors, acquisition noise, etc.; in our experiments N=10 is enough to succeed. The neural networks are trained on a combination of 80% of traces from the profiling device with a known shuffling order and 20% of traces from the device under attack captured for all-0 and all-1 messages. "Spicing" the training set with traces from the device under attack helps minimize the negative effect of device variability.