Waveform Data Augmentation Using Conditional VAE for Deep Learning Side-Channel Attacks against AES Circuit

Abstract

Side-channel attacks (SCAs) have been reported to reveal secret keys by analyzing the power consumption or electromagnetic leakage on cryptographic circuits. Deep-learning SCAs (DL-SCAs) have been actively studied due to their superior performance over conventional attack methods. Conventional attacks use statistical models between power consumption and the internal register values (e.g. the Hamming distance (HD) over consecutive clock is exploited). The attacker can guess the secret key from the internal register values estimated from power consumption. Similarly, neural network (NN) models are constructed to predict the internal register values from the power consumption in the case of a DL-SCA. However, it has been reported that NN models cannot be trained well by using HD as labels because the frequency distribution on each HD is greatly biased. We propose a method of mitigating the class imbalance problem using a conditional variational encoder. We report the results of a DL-SCA using this method against hardware-implemented AES. The method was able to reveal all the partial keys in less than 400 waveforms. Attack performance in this result was better than previous work.