  Recently, many profiling side-channel attacks based on Machine Learning and
Deep Learning have been proposed. Most of them focus on reducing the number of
traces required for successful attacks by optimizing the modeling algorithms.
In previous work, relatively sufficient traces need to be used for training a
model. However, in the practical profiling phase, it is difficult or impossible
to collect sufficient traces due to the constraint of various resources. In
this case, the performance of profiling attacks is inefficient even if proper
modeling algorithms are used. In this paper, the main problem we consider is
how to conduct more efficient profiling attacks when sufficient profiling
traces cannot be obtained. To deal with this problem, we first introduce the
Conditional Generative Adversarial Network (CGAN) in the context of
side-channel attacks. We show that CGAN can generate new traces to enlarge the
size of the profiling set, which improves the performance of profiling attacks.
For both unprotected and protected cryptographic algorithms, we find that CGAN
can effectively learn the leakage of traces collected in their implementations.
We also apply it to different modeling algorithms. In our experiments, the
model constructed with the augmented profiling set can reduce the required
attack traces by more than half, which means the generated traces can provide
useful information as the real traces.
