Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning to reach strong attack performance, which can be far from trivial. Besides many options stemming from the machine learning domain, recent years also brought neural network elements specially designed for side-channel analysis. The loss function, which calculates the error or loss between the actual and desired output, is one of the most important neural network elements. The resulting loss values guide the weights update associated with the connections between the neurons or filters of the deep learning neural network. Unfortunately, despite being a highly relevant hyperparameter, there are no systematic comparisons among different loss functions regarding their effectiveness in side-channel attacks. This work provides a detailed study of the efficiency of different loss functions in the SCA context. We evaluate five loss functions commonly used in machine learning and three loss functions specifically designed for SCA. Our results show that an SCA-specific loss function (called CER) performs very well and outperforms other loss functions in most evaluated settings. Still, categorical cross-entropy represents a good option, especially considering the variety of neural network architectures.

The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various perspectives, like hyperparameter tuning, design guidelines, or custom neural network architecture elements. Still, insufficient attention has been given to the core of the learning process - the loss function. This paper analyzes the limitations of the existing loss functions and then proposes a novel side-channel analysis-optimized loss function: Focal Loss Ratio (FLR), to cope with the identified drawbacks observed in other loss functions. To validate our design, we 1) conduct a thorough experimental study considering various scenarios (datasets, leakage models, neural network architectures) and 2) compare with other loss functions used in the deep learning-based side-channel analysis (both “traditional” ones and those designed for side-channel analysis). Our results show that FLR loss outperforms other loss functions in various conditions while not having computational overhead like some recent loss function proposals.