Paper 2021/1408

Focus is Key to Success: A Focal Loss Function for Deep Learning-based Side-channel Analysis

Maikel Kerkhof, Lichao Wu, Guilherme Perin, and Stjepan Picek

Abstract

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 evaluation method for the evaluation labs/certification schemes. To reach this performance level, recent works significantly improved the deep learning-based attacks from various perspectives, like hyperparameter tuning, design guidelines, or custom neural network architecture elements. Still, limited 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 commonly used in the deep learning-based side-channel analysis (both ``traditional'' one and those designed for side-channel analysis). Our results show that FLR loss outperforms other loss functions in various conditions while not having computation overheads compared to common loss functions like categorical cross-entropy.