Auto-MCNN: Optimizing Multi-Scale CNNs via Automated Machine Learning for Side-Channel Analysis

In recent years, deep learning side-channel analysis (DLSCA) has garnered significant attention, with the choice of model architecture greatly influencing attack efficiency. Currently, convolutional neural networks (CNNs) have become the dominant architecture in the field of side-channel analysis (SCA), and multi-scale CNNs (MCNNs) have gained favor among certain researchers due to their ability to capture information across various scales. However, effectively obtaining multi-scale information from datasets requires the customization of appropriate hyperparameters for each channel, and the hyperparameter tuning process is often time-consuming and labor-intensive. This presents a technical barrier for non-experts or those seeking to simplify their workflow. Such limitations lead researchers to overly rely on fixed hyperparameter models based on specific datasets, overlooking the differences between various data samples, which in turn affects the model’s reusability and generalization capability in broader scenarios. To address these issues, we propose an adaptive MCNN framework based on automated machine learning, named Auto-MCNN. We evaluated the effectiveness of this framework on multiple private and public datasets. To further investigate the variations in the network’s feature extraction capabilities, we employed an improved heatmap visualization method to illustrate the network’s areas of focus. Experimental results demonstrate that the optimized Auto-MCNN model can be more widely applied to the analysis of side-channel leakage traces, significantly enhancing overall analysis efficiency.