Too Hot To Be True: Temperature Calibration for Higher Confidence in NN-assisted Side-channel Analysis

ePrint Report: Too Hot To Be True: Temperature Calibration for Higher Confidence in NN-assisted Side-channel Analysis

Seyedmohammad Nouraniboosjin, Fatemeh Ganji

The past years have witnessed a considerable increase in research efforts put into neural network-assisted profiled side-channel analysis (SCA). Studies have also identified challenges, e.g., closing the gap between metrics for machine learning (ML) classification and side-channel attack evaluation. In fact, in the context of NN-assisted SCA, the NN’s output distribution forms the basis for successful key recovery. In this …

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