Side-Channel Attacks Based on Collaborative Learning

Side-channel attacks based on supervised learning require that the attacker have complete control over the cryptographic device and obtain a large number of labeled power traces. However, in real life, this requirement is usually not met. In this paper, an attack algorithm based on collaborative learning is proposed. The algorithm only needs to use a small number of labeled power traces to cooperate with the unlabeled power trace to realize the attack to cryptographic device. By experimenting with the DPA contest V4 dataset, the results show that the algorithm can improve the accuracy by about 20% compared with the pure supervised learning in the case of using only 10 labeled power traces.