Attacking High-order Masked Cryptosystem via Deep Learning-based Side-Channel Analysis

Masking is widely considered as an effective countermeasure against side-channel analysis (SCA) due to its provable security and efficiency. However, recent works have demonstrated that the deep learning-based SCA (DL-SCA) can effectively break the cryptographic implementations protected by the first-order Boolean maskings. Still, it is open whether higher-order masking can resist DL-SCA. In this work, we demonstrate that deep learning methods can also effectively exploit the inherent leakage of higher-order Boolean masking to compromise its security. Furthermore, we employed neural weight visualization techniques to demonstrate the neural network’s capability to extract high-level features. We assess the efficiency of this novel profiling attack in both simulated and real-world scenarios. In particular, our results show that DL-SCA can effectively break the higher-order Boolean masking schemes up to the sixth and the third order in simulated and real-world cases, respectively. Furthermore, we find that using plaintext-related leakage can significantly improve the effectiveness of side-channel attacks.