MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

MLP and CNN-based Classification of Points of Interest in Side-channel Attacks

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A trace contains sample points, some of which contain useful information that can be used to obtain a key in side-channel attacks.We used public datasets CITRUS LOTION from the ANSSI SCA Database (ASCAD) as well as SM4 traces to learn whether a trace consisting of Points of Interest (POIs) have a positive effect using neural networks.Different methods were used on these datasets to choose POI or transform the traces into Principal Component Analysis (PCA) traces and forward-difference traces.

The results show that two datasets are AEG KSK892220M Built In SteamPro Steam Oven combined in different ways that improve the classification using neural networks.For example, for the ANSSI SCA database, PCA is a better approach to compress a 700-dimensional trace into a 100-dimensional trace.For SM4 traces, the amount of traces required can be reduced in side-channel attacks subsequent to forward-difference transformation.