Paper 2023/209

Hiding in Plain Sight: Non-profiling Deep Learning-based Side-channel Analysis with Plaintext/Ciphertext

Abstract

Deep learning-based profiling side-channel analysis is widely adopted in academia and industry thanks to the ability to reveal secrets protected with countermeasures. To leverage its capability, the adversary needs to have access to a clone of an attack device to obtain the profiling measurements. Moreover, the adversary needs to know secret information to label these measurements. Non-profiling attacks avoid those constraints by not relying on secret information to label data but rather by trying all key guesses and taking the most successful one. Deep learning approaches also form the basis of several non-profiling attacks. Unfortunately, such approaches suffer from high computational complexity and low generality when applied in practice. This paper proposes a novel non-profiling deep learning-based side-channel analysis technique. Our approach relies on the fact that there is (commonly) a bijective relationship between known information, such as plaintext and ciphertext, and secret information. We use this fact to label the leakage measurement with the known information and then mount attacks. Our results show that we reach at least $3\times$ better attack performance with negligible computational effort than existing non-profiling methods. Moreover, our non-profiling approach rivals the performance of state-of-the-art deep learning-based profiling attacks.