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

Enhancing the Performance of Practical Profiling Side-Channel Attacks Using Conditional Generative Adversarial Networks