Deep Learning-based Attacks on Masked AES Implementation

Abstract

To ensure the confidentiality of the message, the AES (Advanced Encryption Standard) block cipher algorithm can be widely used. Furthermore, an implementation of masked AES is often used to resist side-channel attacks. To recover secret keys embedded in cryptographic devices with masked AES, we present some side-channel attacks based on deep learning models in profiling and non-profiling scenarios. The proposed method which applies the mask value profiling technique represents new approaches for extracting the secret key. To defeat the masked AES implementation, deep learning models such as multi-layer perceptron and convolutional neural networks are developed. In a non-profiling scenario, we adopt the DDLA (Differential Deep Learning Analysis) to extract sensitive information such as the secret key. The main idea of our method is that it is possible to adopt a new binary labeling method to conduct the DDLA based on the HW (Hamming Weight) model. We show several experiments using real power traces measured from the ChipWhisperer platform in profiling attacks and the ASCAD dataset in non-profiling attacks respectively. Whether we target naïve or masked AES implementation, the experimental results show the predominant key recovery accuracy.