Multi-Source Training Deep-Learning Side-Channel Attacks
Huanyu Wang, Sebastian Forsmark, Martin Brisfors, Elena Dubrova · 2020
Recently, several deep-learning side-channel attacks on cryptographic algorithms were demonstrated. With the help of a trained deep-learning model, the attacker extracts the key from a few power traces captured from a victim device. However, previous works have shown that the inter-chip variation may significantly reduce the attack success probability. In this paper, we quantify the effect of inter-chip variation on the classification accuracy of Multi-Layer Perceptron (MLP) models. We show that, by training on multiple chips, we can increase the probability of recovering the key from a single trace from 39.95% to 86.07% on average. We also evaluate how the printed circuit board diversity affects the classification accuracy.