Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

Yoo-Seung Won

doi:10.1109/ACCESS.2021.3055833

Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

2021-01-01

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DOI：10.1109/ACCESS.2021.3055833

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Over the years, deep learning algorithms have advanced a lot and any innovation in the algorithms are demonstrated and benchmarked for image classification. Several other field including side-channel analysis (SCA) have recently adopted deep learning with great success. In SCA, the deep learning algorithms are typically working with 1-dimensional (1-D) data. In this work, we propose a unique method to improve deep learning based side-channel analysis by converting the measurements from raw-trace of 1-dimension data based on float or byte data into picture-formatted trace that has information based on the data position. We demonstrate why Picturization is more suitable for deep learning and compare how input and hidden layers interact for each raw (1-D) and picture form. As one potential application, we use a Binarized Neural Network (BNN) learning method that relies on a BNN's natural properties to improve variables. In addition, we propose a novel criterion for attack success or failure based on statistical confidence level rather than determination of a correct key using a ranking system.

Keywords:

Deep learning

Training

Side-channel attacks

Neurons

Entropy

Analytical models

Open area test sites

Binarized neural network

deep learning

multi-layer perceptron

non-profiled side-channel attack

Non-Profiled Side-Channel Attack Based on Deep Learning Using Picture Trace

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