On the Performance of Multilayer Perceptron in Profiling Side-channel Analysis
In profiling side-channel analysis, machine learning-based attacks nowadays offer the most powerful performance. This holds especially for techniques stemming from the neural network family: multilayer perceptron and convolutional neural networks. Convolutional neural networks are often favored as state-of-the-art results suggest better performance, especially in scenarios where targets are protected with countermeasures. Multilayer perceptron receives much less attention and researchers seem less interested in this technique, narrowing the results in the literature to comparisons with convolutional neural networks. Yet, a multilayer perceptron has a much simpler structure, which enables easier hyperparameter tuning, and hopefully, could contribute to the explainability of this neural network inner working. In this paper, we investigate the behavior of a multilayer perceptron in detail in the context of the side-channel analysis of AES. By exploring the sensitivity of multilayer perceptron hyperparameters over the performance of the attack, we aim at providing a better understanding of successful hyperparameters tuning, and ultimately, the performance of this algorithm. Our results show that MLP (with a proper hyperparameter tuning) can easily break implementations having a random delay or masking countermeasures.