# The Need for Speed: A Fast Guessing Entropy Calculation for Deep Learning-Based SCA
## 6 Conclusions and Future Work  
Profiling attacks are important during security evaluations because evaluators can determine if the device leaks information with high assurance. This is especially possible because assumptions during a profiling analysis consider that the target faces an adversary that can learn existing side-channel leakages in a supervised learning setting. In a recent publication [32], [PERSON] et al. showed through the lens of perceived information (PI) [33] how different profiling methods perform against protected cryptographic implementations. Their analysis allows security evaluators to conclude about the target's leakages with the worst-case security. For that, the evaluator assumes that the adversary has knowledge of all intermediate secret shares during profiling and the source code. Consequently, such an evaluation provides conditions to implement optimal profiling models, where assumptions about the target (e.g., leakage model) contain as few errors  
Figure 8: Performance of different validation metrics on state-of-the-art CNN architectures, ASCADf dataset. (**a**) CNN from [8]. (**b**) CNN from [21]. (**c**) Best CNN from [9]. Figure 7: GE results from best models found with BO with different early stopping metrics for the CHES CTF dataset (Hamming weight leakage model). as possible. Moreover, the evaluator can build a profiling model with sufficient traces, thus minimizing estimation errors. The case of deep learning for profiling SCA brings a new perspective on profiling attacks. The main reason for that is related to the ability of a deep neural network to perform efficiently without feature selection. In practice, this means that the attacked interval contains several low SNR points of interest, and selecting the most leaky points of interest with a high SNR becomes more challenging. The advantages for security evaluations come from the fact that neural networks as profiling models can learn existing leakages even without feature selection and, in practice, deliver close to optimal results. The results for CNN architectures from Figure 8 are an example of this case. Of course, to reach an optimal deep learning profiling model, costly hyperparameter tuning needs to be implemented, especially for more protected targets. Therefore, hyperparameter tuning must be as efficient as possible to reach optimal deep learning models without worst-case security assumptions. For that, assessing the model generalization during training becomes crucial, requiring fast and efficient validation metrics. We propose using a fast GE metric that requires significantly fewer validation traces in the GE calculation. Our results indicate that FGE as a validation metric delivers efficient and competitive early stopping results. Our technique is validated in different scenarios and shows good results with negligible time overheads. More precisely, FGE allows up to 16\(\times\) faster guessing entropy calculation, resulting in more hyperparameter tuning experiments or fewer attack traces needed to reach a guessing entropy of 1. Thus, we consider FGE the method of choice for practical deep learning-based SCA hyperparameter tuning. In future works, we will explore the efficiency of different validation metrics in portability settings and with different countermeasures in future work. Additionally, as this work contains results for convolutional neural networks only, it would be interesting to assess FGE performance with architectures such as multilayer perceptrons and residual neural networks. Conceptualization, G.P. ; Methodology, S.P. ; Software, G.P. ; Validation, L.W. and S.P. ; Investigation, G.P., L.W. and S.P. ; Writing--original draft, G.P., L.W. and S.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.