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Assessment of Common Side Channel Countermeasures With Respect To Deep Learning Based Profiled Attacks

A recent line of research has investigated a new profiling technique based on deep learning as an alternative to the well-known template attack. The related published works have demonstrated that Deep Learning based Side-Channel Attacks (DL-SCA) are very efficient when targeting cryptographic implementations protected with the common side-channel countermeasures such as Boolean masking, jitter and random delays insertion. In this paper, we assess the efficiency of this new profiling attack when targeting other commonly involved countermeasures. First, we target a more complex masking scheme based on Shamir's secret sharing and prove that this new profiling approach is still performing well. Second, we conduct a security evaluation of two side-channel countermeasures (shuffling and 1-amongst- $N$) against DL-SCA. The simulation and practical experiments prove, as expected, that these countermeasures are also vulnerable to these profiling attacks.