Welcome to the resource topic for 2022/1507
Title:
AGE Is Not Just a Number: Label Distribution in Deep Learning-based Side-channel Analysis
Authors: Lichao Wu, Léo Weissbart, Marina Krček, Huimin Li, Guilherme Perin, Lejla Batina, Stjepan Picek
Abstract:The efficiency of the profiling side-channel analysis can be improved significantly with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target.
This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. Besides, by studying the relationship between all possible key candidates, we propose a new metric, denoted augmented guessing entropy (AGE), to evaluate the generalization ability of the profiling model. We validate AGE with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.
ePrint: https://eprint.iacr.org/2022/1507
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