Paper 2021/1091
No (Good) Loss no Gain: Systematic Evaluation of Loss functions in Deep Learning-based Side-channel Analysis
Maikel Kerkhof, Lichao Wu, Guilherme Perin, and Stjepan Picek
Abstract
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 for strong attack performance, which can be far from trivial. Besides a plethora of options stemming from the machine learning domain, recent years also brought neural network elements specially designed for side-channel analysis. An important hyperparameter is the loss function, which calculates the error or loss between the actual and desired output. The resulting loss is used to update the weights 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. This work provides a detailed study on the performance of different loss functions in the SCA context. We evaluate five loss functions commonly used in machine learning and two loss functions proposed for SCA. Our results show that one of the SCA-specific loss functions (called CER) performs very well and outperforms other loss functions in most evaluated settings. Finally, our results show that categorical cross-entropy represents a good option for most settings, especially if there is a requirement to work well with different neural network architectures.
Metadata
- Available format(s)
- Category
- Implementation
- Publication info
- Preprint. MINOR revision.
- Keywords
- Side-channel analysisDeep LearningLoss functionEvaluation
- Contact author(s)
-
maikelkerkhof @ gmail com
lichao wu9 @ gmail com
guilhermeperin7 @ gmail com
picek stjepan @ gmail com - History
- 2021-08-25: received
- Short URL
- https://ia.cr/2021/1091
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2021/1091, author = {Maikel Kerkhof and Lichao Wu and Guilherme Perin and Stjepan Picek}, title = {No (Good) Loss no Gain: Systematic Evaluation of Loss functions in Deep Learning-based Side-channel Analysis}, howpublished = {Cryptology {ePrint} Archive, Paper 2021/1091}, year = {2021}, url = {https://eprint.iacr.org/2021/1091} }